Electric vehicle collaborative power grid power supply recovery strategy considering multi-time-domain EV-EPSV

Abstract

An electric vehicle collaborative power grid power supply recovery strategy considering multi-time-domain EV-EPSV comprises the steps that a multi-time-sequence electric vehicle EV equivalent power supply model is established according to a travel rule of an electric vehicle user in a typical travel scene; a load importance index system is constructed, a load power loss model is established in combination with the load capacity and the power failure duration, and quantitative calculation of economic and social losses of different types of loads in the multi-time-sequence power failure process is achieved; and constructing a collaborative optimized power supply model of the electric vehicle EV and the emergency power supply vehicle EPSV, and realizing static power distribution of the electric vehicle and dynamic path and power optimization scheduling of the emergency power supply vehicle. According to the strategy, a multi-time-sequence EV equivalent power supply model is established, a load power loss evaluation system is constructed, and a collaborative optimization scheduling method of the electric vehicle and the emergency power supply vehicle is provided, so that efficient distribution of limited power supply resources and improvement of power supply recovery efficiency are realized. The scheduling flexibility of electric vehicle resources and the power supply toughness of a power grid can be effectively improved in a sudden power failure situation.

Description

Technical Field

[0001] This invention relates to the field of power system and electric vehicle V2G cooperative dispatch technology, specifically to an electric vehicle cooperative grid power supply restoration strategy that considers multi-time domain EV-EPSV. Background Technology

[0002] With the rapid expansion of electric vehicle (EV) fleets and the maturity of V2G technology, EVs are gradually evolving from transportation tools into distributed energy storage and flexible regulation resources within the power system. Leveraging their bidirectional energy flow capabilities, EVs can charge during off-peak hours and discharge during peak hours, participating in peak shaving and supply-demand balancing. In sudden power outages or disaster scenarios, they can also support critical loads through group collaboration, enhancing system resilience. However, existing research primarily focuses on energy optimization and scheduling under normal operating conditions, paying insufficient attention to emergency recovery after power outages. Furthermore, it lacks system modeling for multi-temporal characteristics, seasonal differences, and vehicle dynamic availability, limiting the efficient utilization of EV resources in emergency scenarios.

[0003] Existing emergency power supply models typically employ static or single-moment equivalent power descriptions, which fail to capture the dynamic characteristics of vehicle travel and charging / discharging behavior. This leads to biases in available power assessments and impacts dispatch efficiency. Load-side assessments are also largely based on static or averaging methods, failing to adequately reflect load importance, continuous demand, and temporal variations. Consequently, limited power sources are difficult to allocate accurately, making it challenging to balance reliability and economy. Summary of the Invention

[0004] To overcome the problems of static emergency power supply models for electric vehicles (EVs), incomplete load loss assessment, and lack of multi-time-series adaptability in existing technologies, this invention proposes an EV-EPSV collaborative grid power restoration strategy. This strategy establishes a multi-time-series EV equivalent power source model and constructs a load loss assessment system to propose a method for the collaborative optimization scheduling of EVs and emergency power supply vehicles, thereby achieving efficient allocation of limited power resources and improving power restoration efficiency. It can effectively enhance the scheduling flexibility of EV resources and the resilience of the power grid in the event of a sudden power outage.

[0005] The technical solution adopted in this invention is as follows: Considering the electric vehicle-to-grid power restoration strategy for multi-time-domain EV-EPSV, the following steps are included: Step 1: In the event of a power grid failure, establish a multi-time-series electric vehicle (EV) equivalent power source model based on the travel patterns of EV users in typical travel scenarios; Step 2: Construct a load importance index system, and combine it with load capacity and outage duration to establish a load power loss model, so as to realize the quantitative calculation of economic and social losses of different types of loads in multi-sequence power outage process; Step 3: Construct an optimized power supply model for the collaboration between electric vehicles (EVs) and emergency power supply vehicles (EPSVs) to achieve static power allocation for EVs and dynamic path and power optimization scheduling for emergency power supply vehicles.

[0006] In step 1, based on the dynamic evolution characteristics of electric vehicle users' travel patterns and combined with NHTS2017 travel sample data, the data is divided into summer and winter scenarios by season and into weekday and holiday scenarios by time sequence to complete the data fitting. Then, a vehicle travel state transition matrix is ​​established through a semi-Markov chain (SMC), and the vehicle behavior is stochastically simulated using the Monte Carlo method (MCM) to obtain the available discharge capacity of electric vehicles at each time step. This allows the electric vehicle cluster in the region to be equivalent to a time-varying power source model, and a multi-time series EV equivalent power source model to be established.

[0007] In step 1, in order to characterize the travel patterns of electric vehicle users under different seasons and time periods, the travel sample data of NHTS2017 is first divided into summer and winter scenarios according to season, and into weekday and holiday scenarios according to time period, thus forming four typical travel scenarios.

[0008] For each typical travel scenario, samples of departure and arrival times of electric vehicle users are extracted, and a weighted model is performed using a Mixture of Wrapped Gaussians (MWG) model. The probability density function is shown in Equation (1): (1); In formula (1): For the scene s ,area k Behavioral types q At any moment t The probability density function; Indicates in the scene s ,area k Behavioral types q The number of Gaussian components used below; The weights of each component satisfy the following conditions: , ; and These represent the center (hours) and standard deviation of the component, respectively. To wrap around the Gaussian kernel, so as to achieve 24h periodization and ensure the continuity and normalization of the distribution in the interval [0,24); t This indicates the corresponding intraday time, with a value range of 0–24 hours. s This indicates four typical operating scenarios: summer, winter, weekdays, and holidays. k This represents three typical areas: residential areas, work areas, and shopping areas. q This indicates the behavior type, used to distinguish between two types of electric vehicle usage behaviors: departure and arrival; In Gaussian mixtures, the first... c The index of each Gaussian component.

[0009] To further avoid the distortion of the distribution of electric vehicle departure / arrival times at the 24-hour boundary and to maintain periodic consistency, the wrap-around Gaussian kernel function is defined as shown in equation (2): (2); In formula (2): This is a wrapping Gaussian kernel function with a period of 24 hours; t For time variables (unit: h); μ , σ These are the mean and standard deviation, respectively. m This is a 24-hour shift index used for periodic extension of the time axis; Represents the set of integers, that is Z ={…,−2,−1,0,1,2,…}.

[0010] When the sample size is small or there is morphological skew, the Periodic Kernel Density Estimation (PKDE) method is used for smooth estimation, and its expression is shown in Equation (3): (3); In formula (3): In the scene s ,area k Behavioral types q The periodic kernel density function; This represents the total number of samples used for periodic kernel density estimation; h For bandwidth parameters; Indicates the first i Each sample time point; m ∈{−1,0,1} is a periodic translation term used to smoothly connect at the boundary [0,24]. This represents an exponential function with base e, i.e., 𝑒 (⋅) .

[0011] By combining equations (1) to (3) with the Gaussian mixture model and the periodic kernel density estimation, a time distribution function that is both smooth and consistent with the 24-hour cycle can be obtained under different sample sizes and morphological conditions, providing a unified input for subsequent statistical modeling of travel chains and dwell time.

[0012] In step 1, after data fitting is completed, a mathematical model of multi-time-series electric vehicle travel behavior is established based on the vehicle transition kernel matrix of a semi-Markov chain (SMC) and the Monte Carlo method (MCM). To avoid symbol redundancy, scenario subscripts are omitted in the derivation of the semi-Markov chain transition matrix and the Monte Carlo region state matrix below. s However, the actual calculations are constructed under four typical scenarios (summer, winter, weekday, and holiday). First, the time-related transition probability of the vehicle between three areas—residential area, work area, and shopping area—is described by a semi-Markov chain. Then, the definition of the vehicle semi-Markov kernel matrix is ​​as shown in equation (4): (4); In equation (4): For a moment t The semi-Markov transition kernel matrix; Indicates that electric vehicles are in the area m The stay time shall not exceed t In the case of transferring to the region n The probability, it also characterizes the probability from m arrive n The transition probability and the residence time distribution in this state, where, m , n ∈{1,2,3}, areas 1 / 2 / 3 correspond to residential area / work area / shopping area respectively.

[0013] After obtaining the transition kernel matrix, in order to obtain the group-level time-series trajectory samples, the Monte Carlo Method (MCM) is used to randomly sample and evolve the vehicles' states. The vehicle state matrix for 24 hours in a day is defined as shown in Equation (5): (5); In equation (5): For the vehicle state matrix; Indicates the first j electric vehicles at all times t The area i ,in, i =1,2,3: This represents the area code, where 1 represents a residential area, 2 represents a work area, and 3 represents a shopping area. t=1,2,…,24: represents the 24 hourly times of the day; j Indicates the first j A car, j =1,2,…, , This indicates the total number of electric vehicles included in the statistical sample.

[0014] The semi-Markov kernel matrix of the vehicle given in equation (4) provides a probability-driven approach, while the vehicle state matrix given in equation (5) carries the temporal distribution of the group obtained through Monte Carlo sampling. Together, they constitute the core input and output of the multi-temporal electric vehicle travel chain model and provide a complete spatiotemporal statistical basis for the equivalent power source model.

[0015] In step 1, for the sake of simplicity, the scene subscript will not be explicitly marked below. s However, all calculations are performed separately for each of the four scenario types. Based on this vehicle state matrix... It is possible to count the number of vehicles and their proportion in each region at any given time, and then construct the available power model of the electric vehicle group, as shown in equation (6): (6); In formula (6): For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. For the car at the time t The regional status; k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index.

[0016] According to the definition in equation (6), the number and proportion of vehicles in the area can be calculated, as shown in equations (7) and (8) respectively: (7); (8); In the above formula: Let be the number of vehicles in region k at time t; This represents the percentage of vehicles in the area. For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car int Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. This represents the total number of electric vehicles included in the statistical sample.

[0017] The above results characterize the spatial distribution of vehicles over a 24-hour period, laying the foundation for calculating the available power of the electric vehicle group. In power aggregation calculations, not all vehicles in stationary areas can immediately participate in V2G discharge. Discharge availability is constrained by the battery's state of charge (SOC). Let the upper and lower SOC thresholds be... Y 1 and Y 2, then the first j The car at any time t The discharge state function is defined as shown in equation (9): (9); In equation (9): It is the discharge state function; For the first j The state of charge of the vehicle; and These are the lower discharge threshold and the upper charge threshold, respectively, satisfying... .when At that time, the vehicle is allowed to discharge; when At this time, charging is only allowed; when When the value is between the two thresholds, the vehicle is in standby mode and is not included in the discharge set.

[0018] Combining equations (6) and (9), the region can be calculated. k At any moment t The number of vehicles that can participate in the discharge is shown in equation (10): (10); In formula (10): For the region k At any moment t Number of vehicles capable of discharging electricity; For the region indicator function, indicating the first j The car at any time t Is it stationary and located in the area? k ; Let {1} be the discharge state function; let {1} be the indicator function, if and only if the vehicle satisfies the discharge availability condition of equation (9). The value is 1.

[0019] To further consider the randomness of vehicle participation in V2G behavior, a Bernoulli random variable is introduced. This indicates whether the vehicle participates in the actual discharge. It follows the parameter... The Bernoulli distribution is shown in equation (11): (11); In equation (11): Indicates the time of the j-th car t Whether or not it participates in the discharge: 1 for participation, 0 for non-participation; The average participation rate of the group reflects the probability that a vehicle will participate in V2G when it is in a state where it can be discharged. This represents a Bernoulli distribution, and the output is a random variable of 0–1.

[0020] Based on equation (11), the expected number of vehicles that can be discharged at a given time in the region can be calculated, as shown in equation (12): (12); In equation (12): For expectation operators; Indicates the proportion of group discharge participation; Indicates the area k At any moment t The number of vehicles; For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. Indicates the time of the j-th car t Whether or not it participates in the discharge, 1 means participating, 0 means not participating.

[0021] Equation (12) shows that under large sample conditions ( The average effect of random participation in a group is equivalent to that of proportional participation. Scaling the number of vehicles occupying a region provides a resolvable input for subsequent power aggregation.

[0022] Once the number of vehicles capable of discharging is determined, power layer aggregation can be further performed. Let the rated discharge power of a single vehicle be... Then the region k At any moment t The instantaneous equivalent output power is shown in equation (13): (13); In equation (13): For the region k The equivalent discharge power; This refers to the rated discharge power of a single vehicle. The total number of electric vehicles included in the statistics; approximation symbol " "This means that, in the sense of the population average, the expected value is used to replace the fluctuations caused by sample randomness.

[0023] At the population average level, taking the mathematical expectation of equation (13), we can obtain the expected form of regional power as shown in equation (14): (14); In equation (14): For the region at time t k The average equivalent power; For at any time t area k The mathematical expectation of the total number of vehicles in the system; This represents the percentage of vehicles in the area.

[0024] Finally, by superimposing the power of the three types of areas—residential area, work area, and shopping area—the total equivalent output power of the system can be obtained, as shown in equation (15): (15); In equation (15): For the system at time t Total equivalent output power.

[0025] By combining vehicle travel chains, SOC constraints, participation probabilities, and power aggregation using formulas (1) to (15), a multi-time-series electric vehicle (EV) equivalent power source model was established to describe the temporal behavior characteristics of the group. This model can dynamically characterize the available discharge capacity of electric vehicle groups in different regions, providing a reliable quantitative basis for load regulation and energy storage optimization on the grid side.

[0026] In step 2, a load loss assessment system is established to quantify the economic and social impacts of different types of loads during power outages. This system includes: constructing a load importance index system by comprehensively considering multiple factors such as load life safety, economic losses, and social impacts; and establishing a load loss model by combining load capacity and outage duration. This enables the quantitative calculation of economic and social losses of different types of loads during multi-sequence power outages, providing a basis for decision-making regarding the allocation and restoration of limited power sources.

[0027] In Step 2, load loss refers to the economic and social impact of power outages on loads during the period from power outage to restoration. First, a load importance index system is established, dividing load importance into three categories: Life / Safety Loss Parameters This is used to describe loads such as medical care and emergency support that have a critical impact on personal safety; Economic loss parameters This reflects the economic losses caused by power outages to enterprise production and business activities; Special / Social Impact Parameters It is used to characterize the loads of public services, transportation facilities, and other facilities that are of particular importance to the operation of society; Set load type k ={Residence, Work, Shopping}; Scenarios s ={Summer, Winter, Weekdays, Holidays}. Importance parameter vectors are set for each type of load. And introduce a safety amplification factor that only applies to the life / safety dimension. (Default is 2.0). Based on this definition, the important parameters are as shown in the following equation (16): (16); In equation (16): This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. This is a safety amplification factor that only applies to the life / safety dimension; These are parameters for this type in terms of life / safety, economics, and special / social impact.

[0028] Equation (16) can comprehensively measure the multidimensional sensitivity of load to safety, economy and society, and provide a priority quantitative basis for subsequent loss assessment.

[0029] In step 2, to characterize the cumulative characteristics of economic losses during a power outage over time, a load loss loss modeling system based on the time-increase rate method is established, specifically including: Let the rate of change of economic loss due to load power loss between adjacent time nodes be the load loss growth rate, and its calculation method is shown in Equation (17): (17); In equation (17): For load type k In the interval Internal loss growth rate (unit: yuan / kW·min); Indicates at time Time type k The cumulative cost of load loss; Indicates at time Time type k The cumulative cost of load loss; t s , t s+1 These represent adjacent calculation times; s For the first s Indexes for time intervals.

[0030] To further reflect the role of different types of load capacity in losses, a load importance weighting method is introduced for comprehensive calculation, as shown in equation (18): (18); In equation (18): For load type k Duration of the power outage d Cumulative losses within the period (unit: yuan); For type k Load capacity; This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. For load type k In the interval Internal loss growth rate (unit: yuan / kW·min); Representing the interval [0,d] and the segment Effective overlap duration; This indicates the number of interval segments, which means dividing the power outage process into 7 time periods for segmented accumulation of losses.

[0031] In summary, the load importance index system and time rate loss model established in step 2 can dynamically reflect the differences in power outage duration, capacity and importance of different loads, providing a scientific and quantifiable decision-making basis for subsequent power optimization allocation and rapid recovery strategies.

[0032] In step 2, seasons refer to summer and winter, days to holidays and weekdays, and regions to residential areas, work areas, and shopping areas. In the above scenarios involving multiple seasons, days, and regions, to facilitate comparison of the economics of different power restoration strategies—EV-only power supply and EV-EPSV coordinated power supply—it is necessary to first uniformly model the cost composition of the system restoration process. The overall economic cost of the system restoration process mainly consists of three parts: switching operation cost S, mobile power supply usage cost, and load power loss loss. The switching operation cost S is used to describe the operational cost of the system during network reconfiguration; The cost of using mobile power supplies is used to quantify the comprehensive costs incurred during the discharge of electric vehicles (EVs) and the replenishment and maintenance of emergency power supply vehicles (EPSVs), and is treated as a unified cost item in the model. Load loss reflects the economic and social impact of the load during the period from power outage to restoration; Since different nodes are located at varying distances from the power source, and cost is positively correlated with distance, the cost can be... jThe operating cost of a switch is modeled as being proportional to the distance, as shown in equation (19): (19); In equation (19): α is the operating cost coefficient corresponding to a unit electrical distance, which is used to characterize the increased operating cost for each additional unit electrical distance; For the first j The electrical distance between the power source of a switch and the electrical load can be the impedance distance, the line length, or the distance converted by the equivalent voltage level. For the first j The cost of a single operation of a switch is used to quantify the economic cost of that switch during fault isolation and power restoration.

[0033] After obtaining the operating cost of a single switch, assume there are a total of [costs] during the recovery period. If there are multiple operating switches, the total cost of switch operation can be expressed as shown in equation (20): (20); In equation (20): S Total cost of switch operation; n s Number of operating switches; For the first j The cost of a single operation of a switch.

[0034] While considering the cost of switching operations, it is also necessary to consider the comprehensive costs incurred during the discharge of electric vehicles (EVs) and the replenishment and maintenance of emergency power supply vehicles (EPSVs). This step provides a unified calculation method for these costs. The cost of using a mobile power source is composed of the costs of regular use. With compensation costs It consists of two parts, and the conventional usage cost is shown in equation (21) below: (twenty one); In equation (21): C 1 represents the typical operating cost of a mobile power bank collection (EV and EPSV) over a single power supply cycle; W The cost of unit equivalent electrical energy (unit: yuan / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M The unified operation and maintenance costs for EVs and EPSVs.

[0035] If some mobile power sources need to be replenished with energy during the later stages of recovery (grid power replenishment for EVs and fuel supply for EPSVs), the compensation cost is shown in equation (22): (twenty two); In equation (22): C 2 represents the compensation costs incurred by the mobile power bank collection (EV and EPSV) in restoring usable capacity after a power outage event; W The cost of unit equivalent electrical energy (unit: yuan / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 represents the EV discharge electricity price or EPSV fuel conversion cost.

[0036] In normal usage costs C 1 and compensation costs C Based on the given information, the total cost of using the mobile power supply set (EV and EPSV) in a single power supply cycle can be expressed as the sum of the two, as shown in equation (23): (twenty three); In equation (23): The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle; The typical operating cost of a mobile power bank collection (EV and EPSV) over a single power supply cycle; Compensation costs incurred by mobile power bank collections (EVs and EPSVs) to restore available capacity after a power outage event; W The cost of unit equivalent electrical energy (RMB / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M The unified operation and maintenance costs for EVs and EPSVs.

[0037] Besides the switching operation costs and the cost of using a mobile power supply, the economic losses caused by the load's inability to obtain normal power supply during a power outage are also significant. The power deficit of the system at any given time can be represented by the difference between the demand power and the actual power supplied, as shown in equation (24): (twenty four); In equation (24): For the system at time t The power gap represents the unmet load demand at that moment; For the region k At any moment t The load demand power; The available power provided by the EV vehicle.

[0038] To convert the power deficit into an economic loss rate, a loss growth rate function is introduced. This reflects the economic loss caused by a unit power shortage per unit time. The system at time... t The economic loss rate can be defined as shown in equation (25): (25); In equation (25): For the system at time t Economic loss rate (unit: yuan / min); For the system at time t The power gap represents the unmet load demand at that moment; It is the loss growth rate function of the system at time t (unit: yuan / (min·kW)), used to quantify the sensitivity of the power outage duration to economic losses, such as the impact of peak hours on critical loads. The load will be significantly higher than during normal or off-peak periods, and its value can be determined by the segmented increment rate for each load type. The weighted average is obtained.

[0039] Integrating the instantaneous loss rate over the entire power outage period yields the cumulative economic loss of the system during the power outage event, as shown in equation (26): (26); In equation (26): For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; T This represents the maximum duration of this power outage event; For the system at time t The rate of economic loss.

[0040] After comprehensively considering the economic losses from the outage, the switching operation costs, and the V2G usage costs of electric vehicles, the overall power loss loss of this power outage event can be uniformly expressed as shown in equation (27): (27); In equation (27): The total power loss of the system during a single power outage-recovery process; For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; S Total cost of switch operation; Total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle.

[0041] Based on the above modeling, a load loss loss model was established, providing a common evaluation basis for the subsequent optimization of power restoration strategies for EV-only power restoration and EV-assisted emergency power supply vehicle (EPSV) power restoration.

[0042] In step 3, in the recovery scenario where only electric vehicles (EVs) participate in power supply, a genetic algorithm (GA) is used to search for the optimal spatiotemporal allocation strategy of electric vehicles (EVs) within a 24-hour period. The overall power loss of the system is used as the fitness, and the global optimal solution is approximated through selection, crossover, and mutation iteration.

[0043] To reflect the differences in reliability and economy among different nodes, this step further introduces a comprehensive importance weight for the nodes. and node economic loss growth rate Weighting the power gap, the power loss expression for the system under the electric vehicle (EV) scheme is as follows (28): (28); In equation (28): This refers to the total power loss of the system when only electric vehicles participate in power restoration; Assuming a comprehensive importance weight for each node, Increased rate of economic loss at nodes; and They are nodes at time 10:00 and 20:00 respectively. t The required power and recovery power; S Total cost of switch operation; The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle.

[0044] Based on this, a comprehensive optimization objective function for the EV scenario only is constructed, as shown in equation (29): (29); In equation (29): This is the comprehensive objective function value in the EV scenario only, and it is also the evaluation index of individual fitness in GA. The smaller the value, the better the solution. For the first j Power supply to load node a The shortest electrical distance (unit: km); For nodes j Power supply capacity (unit: kW); This refers to the total power loss of the system when only electric vehicles participate in power restoration; , These are the weighting coefficients for distance cost and power outage loss, respectively.

[0045] To ensure that the solution meets the physical and operational constraints, the power allocation of EV needs to be constrained during the GA iteration process. The typical constraint can be expressed as shown in equation (30): (30); In equation (30): For the first j Vehicles towards the area k Power allocation ratio coefficient of power supply; The available power provided by the EV vehicle; For the region k The load demand power. The first constraint states that the sum of the vehicle power allocation ratio coefficients shall not exceed 1 to ensure the vehicle power supply capacity constraint; the second constraint states that the node recovery power shall not be negative, nor shall it exceed 1.1 times the demand power to ensure system voltage stability.

[0046] This step, within the framework of genetic algorithms, achieves the optimal temporal and spatial configuration of EV power through multi-generational evolutionary search. While relying solely on EVs for power supply can achieve localized power restoration to some extent, this approach is still constrained by both capacity and spatial distribution: on the one hand, the remaining capacity of EVs available for V2G discharge is limited, making it difficult to support long-term, large-scale load restoration; on the other hand, the spatiotemporal distribution of EVs is highly uneven across different regions, potentially leading to insufficient available vehicles in densely loaded areas and idle vehicles in non-critical areas, thus limiting overall power supply capacity and compromising restoration efficiency. Therefore, the EV-only approach is insufficient to address the restoration needs of diverse load types, cross-regional operations, and long durations. It is necessary to further introduce the mobile energy replenishment capabilities of Emergency Power Supply Vehicles (EPSVs) to construct a more resilient collaborative power restoration strategy.

[0047] In step 3, to overcome the capacity and space limitations of the EV-only solution, this step further introduces Emergency Power Supply Vehicles (EPSVs) to participate in power supply scheduling, forming a collaborative optimization strategy of static EV allocation and dynamic EPSV compensation. In the first stage, a Genetic Algorithm (GA) completes the initial power and node allocation for the EVs; in the second stage, an Adaptive Large Neighborhood Search (ALNS) algorithm is introduced to dynamically optimize the driving path and stopping order of the EPSVs.

[0048] Based on this, the EV group provides initial support, while EPSV provides mobile support to key nodes. Together, they form a regional collaborative power supply capability. The micro power distribution form is shown in Equation (31): (31); In equation (31): For the region k At any moment t The equivalent combined power supply; Power allocation factor; For nodes j The equivalent power; For emergency vehicles v To load k Power supply capacity.

[0049] To simplify subsequent derivations, EV and EPSV are compared to the region. k The provided power is denoted as follows: and The equivalent representation of the combined power supply is obtained, as shown in equation (32): (32); In equation (32): For the region k At any moment t Combined power supply of EV-EPSV; For the region k At any moment t Power supplied by the EV group; For the region k At any moment t Power supplied by EPSV.

[0050] Since the importance and load scale of nodes vary in different regions, a regional weighting coefficient is introduced to reflect this unevenness in the system evaluation. The region weight is defined as shown in equation (33): (33); In equation (33): For the region k The weighted importance coefficient comprehensively reflects the load scale and the weight of key nodes in the region; For the region k Set of internal nodes; For nodes i Importance weight; For nodes i The required power.

[0051] To maintain symbol consistency, the region k The total power demand is denoted as Its definition is shown in equation (34): (34); In equation (34): For the region k At any moment tThe required power; For the region k At any moment t The load demand power.

[0052] Based on the above definitions, and considering the differences in load scale and critical node distribution across different regions, a regional weighting coefficient is introduced. Weighting the power deficit, the overall power loss of the system under the EV-EPSV collaborative recovery scheme is shown in equation (35): (35); In equation (35): The overall power loss loss under the EV and EPSV collaborative power supply scheme; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply for EV and EPSV; S Total cost of switch operation; The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle.

[0053] When considering the balance between fairness and economy of power supply resources, a corresponding comprehensive optimization objective function is constructed. Then, the comprehensive optimization objective in the EV-EPSV collaborative scenario can be expressed as shown in equation (36): (36); In equation (36): The comprehensive objective function value under the EV–EPSV collaborative power supply scheme is the fitness evaluation index in the ALNS search process; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply of the EV and EPSV; λ is the weighting coefficient; The proportion of power supplied to the target node; The total nominal power supply capacity (maximum power supply capacity) available for the EV-EPSV vehicle group during the cooperative power supply phase. The first item represents the residual load loss item; the second item represents the fairness penalty item.

[0054] To ensure that the optimization results are physically and operationally feasible, it is also necessary to introduce constraints on the EPSV output and service relationship, as shown in equation (37): (37); In equation (37): The combined power supply for EV and EPSV; For the region k At any moment t The required power; For emergency vehicles v To load k Power supplied; This represents the vehicle's maximum output capacity. A binary variable indicating whether the vehicle is enabled: 1 indicates enabled, 0 indicates disabled; Constraint variables are assigned to the power supply task. The first constraint states that the combined power supply power of the region cannot be negative and cannot exceed the power demand of the region, so as to ensure that the power supply level of each region is physically feasible and meets the basic supply and demand balance constraint. The second constraint states that the power provided by the emergency power supply vehicle to the region shall not exceed the vehicle's maximum output capacity and shall be consistent with the start-stop state variable, so as to ensure that the output of a single vehicle does not exceed the limit and conforms to the vehicle's activation state. The third constraint states that the sum of the service allocation variables of each region at the same time does not exceed 1, so as to ensure that each region is served by at most one emergency power supply vehicle, thereby ensuring that task allocation and route scheduling are feasible in actual operation.

[0055] To quantify the economic differences between the two strategies across multiple scenarios (summer, winter, holidays, weekdays) and multiple regions (residential areas, work areas, commercial areas), a relative savings rate index is introduced. As shown in equation (38): (38); In equation (38): Indicates in the scene s area a The relative savings rate of the EV-EPSV collaborative power supply solution compared to the EV-only solution. The larger the value, the more obvious the economic advantage of the collaborative solution in this spatiotemporal scenario; Indicates in the scene s area a The overall power loss of the EV solution only; Indicates in the scene s area a Overall power loss of the EV-EPSV collaborative scheme; s Indicates the scene type (summer, winter, weekday, holiday); a Indicates the area category (residential area, work area, shopping area).

[0056] In step 3, seasonal, daily and regional load characteristics are taken into account, and an optimized power supply model for the coordinated operation of electric vehicles (EV) and emergency power supply vehicles (EPSV) is constructed based on formulas (19) to (38).

[0057] The first stage uses a genetic algorithm (GA), which uses formulas (19) to (30) to achieve static power allocation of electric vehicles in order to improve local power utilization. The second stage is based on the Adaptive Large Neighborhood Search (ALNS) algorithm, which uses formulas (31) to (38) to realize the dynamic path and power optimization scheduling of the emergency power supply vehicle, so as to improve the overall power supply restoration speed and system economy.

[0058] This invention provides a cooperative power grid power supply restoration strategy for electric vehicles considering multi-time-domain EV-EPSV, with the following technical advantages: 1) This invention establishes a multi-temporal EV equivalent power supply model considering vehicle travel patterns. By introducing a semi-Markov chain and Monte Carlo stochastic simulation method, the model characterizes the distribution characteristics of available energy storage capacity of electric vehicle groups under different scenarios. This model can reflect the energy storage patterns of residential areas, work areas, and shopping areas under different seasons (summer, winter) and day types (weekdays, holidays), realizing a dynamic quantitative assessment of the potential of available power supply for EVs. The advantages of this modeling method are: ① It overcomes the shortcomings of traditional static capacity models that cannot reflect the spatiotemporal travel characteristics of vehicles; ② Through multi-temporal probabilistic modeling, the energy storage dispatchability assessment is closer to real operation; ③ It provides a quantifiable spatiotemporal energy supply boundary for subsequent emergency power supply scheduling.

[0059] 2) This invention constructs a multi-dimensional load power outage loss assessment system, realizing the comprehensive quantification of load importance and time dimension. By introducing a load importance index system, a quantitative calculation model for load power outage loss is established. The model is based on load type, outage duration, and regional characteristics, comprehensively considering three dimensions: life safety, economic loss, and social impact, and realizes the priority division of loads in multiple regions.

[0060] 3) This invention proposes a multi-time-sequence electric vehicle collaborative emergency power supply optimization strategy to achieve multi-stage and multi-regional dynamic power supply restoration. This strategy comprehensively considers the complementary characteristics of electric vehicles (EVs) and emergency power supply vehicles (EPSVs), constructing a two-stage collaborative optimization model based on a genetic algorithm (GA) and an adaptive large neighborhood search algorithm (ALNS): In the first stage, GA optimizes the static power allocation and node scheduling of EVs to improve local power supply coverage and energy utilization; in the second stage, ALNS optimizes the dynamic path and power allocation of EPSVs to achieve precise power supply replenishment to key areas. This collaborative model balances the timeliness and economy of system power supply, enabling a multi-stage recovery mechanism of "rapid response followed by flexible replenishment" after a grid failure.

[0061] 4) This invention achieves efficient and economical power restoration in multiple scenarios. Simulation analysis results across multiple scenarios (summer, winter, holidays, weekdays) and regions (residential areas, work areas, commercial areas) show that the collaborative restoration strategy improves the overall energy saving rate by 34%–70% compared to relying solely on the EV solution. Residential areas show the highest average energy saving rate (approximately 65%), work areas perform best on weekdays (approximately 50%), and shopping areas show a significant advantage on holidays (approximately 60%). This technology achieves continuity and smoothness in the power restoration process through rapid EV activation and subsequent EPSV support, effectively reducing energy lag and voltage fluctuations, thereby improving system resilience and operational stability.

[0062] 5) This invention forms a closed-loop system of model-evaluation-strategy, significantly improving the dispatchability and grid resilience of electric vehicles in emergency power supply. The strategy theoretically improves the research system for the coordinated recovery of distributed energy storage and mobile power sources, and has good scalability in engineering. This method ensures the reliability of emergency power supply while taking into account economy, flexibility, and universality, providing theoretical support and technical implementation path for the construction of new resilient distribution networks. Attached Figure Description

[0063] The present invention will be further described below with reference to the accompanying drawings and examples; Figure 1 Heat maps showing the entry and exit behavior and dwell time of electric vehicles in residential, work, and shopping areas during the summer.

[0064] Figure 2 Heat maps showing the entry and exit behavior and dwell time of electric vehicles in residential, work, and shopping areas during winter.

[0065] Figure 3 Heatmaps showing the entry and exit behavior and dwell time of electric vehicles in residential, work, and shopping areas during weekdays.

[0066] Figure 4 Heat maps showing the entry and exit behavior and dwell time of electric vehicles in residential, work, and shopping areas during holidays.

[0067] Figure 5 This is a graph showing the equivalent power output of electric vehicles in residential, work, and shopping areas during the summer.

[0068] Figure 6 This is a graph showing the equivalent power output of electric vehicles in residential, work, and shopping areas during winter.

[0069] Figure 7 This is a graph showing the equivalent power output of electric vehicles in residential, work, and shopping areas during weekday scenarios.

[0070] Figure 8 This is a graph showing the equivalent power output of electric vehicles in residential, work, and shopping areas during holiday scenarios.

[0071] Figure 9 A schematic diagram of the network topology for improving the IEEE 33-node distribution network system.

[0072] Figure 10 This is a graph showing how load loss varies with the duration of power outages in three types of areas: residential, work, and shopping.

[0073] Figure 11 This is a comparison chart showing the power loss in a residential area using a collaborative power supply restoration strategy versus a traditional EV-only power supply solution during summer.

[0074] Figure 12 This is a comparison chart showing the power loss of the collaborative power supply recovery strategy for the work area and the traditional EV-only power supply solution in a summer scenario.

[0075] Figure 13 This chart compares the power loss of a shopping area collaborative power restoration strategy with a traditional EV-only power supply solution in a summer scenario.

[0076] Figure 14 This chart compares the power loss losses of a residential area collaborative power supply restoration strategy with a traditional EV-only power supply solution during weekday scenarios.

[0077] Figure 15 This chart compares the power loss losses of the collaborative power supply recovery strategy for the work area with the traditional EV-only power supply solution in a weekday scenario.

[0078] Figure 16This chart compares the power loss in a shopping area using a collaborative power restoration strategy versus a traditional EV-only power supply solution during weekday scenarios.

[0079] Figure 17 This is a flowchart of the GA-ALNS two-stage optimization algorithm for the multi-time-domain EV-EPSV collaborative power supply recovery strategy of the present invention. Detailed Implementation

[0080] This paper considers a multi-time-domain EV-EPSV collaborative power grid restoration strategy. First, based on the typical travel patterns of EVs in different seasons (summer and winter) and different day types (weekdays and holidays), a multi-time-series EV equivalent power source model is constructed, and the power supply capacity of the vehicles is modeled through vehicle parking characteristics. Second, for three typical areas—residential areas, work areas, and shopping areas—a multi-time-series load loss assessment system is established based on load capacity, importance, and outage duration, providing a quantitative basis for the subsequent zonal allocation of limited power supply resources. Finally, based on the multi-time-series EV equivalent power source model and the load loss assessment system, a multi-objective optimization model involving EVs and EPSVs is constructed. A genetic algorithm (GA) is used to complete the initial allocation of EVs, and then an adaptive large neighborhood search algorithm (ALNS) is used to optimize the path and scheduling tasks of EPSVs, aiming to minimize system load loss and improve power restoration efficiency. Multi-scenario simulations are conducted on the IEEE-33 node system to verify that the collaborative strategy of this invention has significant advantages in terms of restoration speed, power supply continuity, and economy.

[0081] (I) Multi-sequence EV equivalent power supply model: The multi-time series EV equivalent power supply model is used to characterize the spatiotemporal distribution characteristics and power supply capabilities of electric vehicles under different seasons, different day types, and different functional areas. This embodiment first divides the travel sample data of NHTS2017 into summer and winter scenarios according to season, and into weekday and holiday scenarios according to day type, thus forming four typical travel scenarios. For each scenario, the user departure and arrival time samples are extracted, and a weighted model is first performed using a Mixture of Wrapped Gaussians (MWG) model, the probability density function of which is shown in Equation (1): (1); In formula (1): For the scene s ,area k Behavioral types q (Departure / Arrival) at the time t The probability density function; Number of mixed components; The weights of each component satisfy the following conditions: , ; and These represent the center (hours) and standard deviation of the component, respectively. To wrap around the Gaussian kernel, so as to achieve 24h periodization and ensure the continuity and normalization of the distribution in the interval [0,24); t This indicates the corresponding intraday time, with a value range of 0–24 hours. s This indicates four typical operating scenarios: summer, winter, weekdays, and holidays. k This represents three typical areas: residential areas, work areas, and shopping areas. q This indicates the behavior type, used to distinguish between two types of electric vehicle usage behaviors: departure and arrival.

[0082] To further avoid distribution distortion at the 24-hour boundary and maintain periodic consistency, the wrap-around Gaussian kernel function is defined as shown in equation (2): (2); In formula (2): This is a wrapping Gaussian kernel function with a period of 24 hours; t For time variables (unit: h); μ , σ These are the mean and standard deviation, respectively; m is the 24-hour shift index, used for periodic extension of the time axis.

[0083] When the sample size is small or there is morphological skew, the Periodic Kernel Density Estimation (PKDE) method is used for smooth estimation, and its expression is shown in Equation (3): (3); In formula (3): For the scene s ,area k Behavioral types q The periodic kernel density function; N Total sample size; h For bandwidth parameters; t j For the first j Each sample time point; m ∈{−1,0,1} is a periodic translation term used to smoothly connect at the boundary [0,24].

[0084] By using equations (1) to (3), and through joint modeling with the Gaussian mixture model and periodic kernel density estimation, a time distribution function that is both smooth and consistent with the 24-hour period can be obtained under different sample sizes and morphological conditions, providing input for subsequent vehicle travel chain probability transfer modeling.

[0085] After completing the data fitting, assuming that 10,000 electric vehicles participate in the operation scheduling within the study area, and the scheduling period is 24 hours a day, a mathematical model of multi-time-series electric vehicle travel behavior is established based on the vehicle transition kernel matrix of a semi-Markov chain (SMC) and the Monte Carlo method (MCM). To avoid symbol redundancy, the scene subscripts are omitted in the derivation of the semi-Markov chain transition matrix and the Monte Carlo region state matrix below. s However, the actual calculations are constructed under four typical scenarios (summer, winter, weekday, and holiday). First, a semi-Markov chain is used to describe the time-related transition probabilities of the vehicle between three areas (residential area, work area, and shopping area), and its vehicle state kernel matrix is ​​defined as shown in equation (4): (4); In equation (4): For a moment t The semi-Markov transition kernel matrix; ( m , n ∈{1,2,3}) indicates that the electric vehicle is in the region m At that time, the stay time shall not exceed t In the case of transferring to the region n The probability, which simultaneously characterizes the probability from m arrive n The transition probability and the residence time distribution in this state; regions 1 / 2 / 3 correspond to residential area / work area / shopping area respectively.

[0086] After obtaining the transition kernel matrix, the Monte Carlo Method (MCM) is used to randomly sample and evolve the vehicles' states. For ease of statistical analysis and subsequent power aggregation, a vehicle state matrix is ​​defined for each of the 24 hours of the day, as shown in equation (5): (5); In formula (5): For the vehicle state matrix; Indicates the first j electric vehicles at all times t The area i ,in: i =1,2,3: This indicates the area code (1 represents the residential area, 2 represents the work area, and 3 represents the shopping area). t =1,2,…,24: represents the 24 hourly times of the day; j =1,2,…, : indicates the first j A car.

[0087] Based on equations (4) and (5), multiple rounds of Monte Carlo simulations are performed. By statistically analyzing the number of entries and exits and the dwell time in different areas under each scenario at each time point, the following results can be obtained: Figures 1-4 Heatmaps showing the entry and exit behavior and dwell time of electric vehicles in residential, workplace, and shopping areas under four typical time-series scenarios (summer, winter, holidays, and weekdays). Figures 1-4 It can be seen that: in residential areas, the probability of vehicle presence is highest in the early morning and at night, showing a clear bimodal structure of early departure and late return; in work areas, vehicles are highly concentrated during the day on weekdays but almost emptied at night, with presence characteristics highly consistent with the office cycle; in shopping areas, vehicle presence is relatively dispersed, with a significantly increased probability of presence from afternoon to evening on holidays, reflecting the high activity level of commercial activities during holidays. These spatiotemporal distribution characteristics provide a statistical basis for subsequently equating vehicle behavior with time-varying power sources.

[0088] Based on the aforementioned multi-scenario travel time modeling and travel chain simulation, and using the time distribution of NHTS sample data, a time series simulation was performed combining a semi-Markov chain (SMC) and the Monte Carlo method (MCM) to obtain the regional state matrix of electric vehicles in a 24-hour discrete time series. Therefore, we can obtain the first... j The car at the top of the hour t Region state ∈{1,2,…,24} Z j (t)∈{1,2,3}, where 1, 2, and 3 represent the residential area, the work area, and the shopping area, respectively. This state matrix is ​​generated by SMC transition probabilities and MCM random sampling, providing basic data for constructing a multi-time-series electric vehicle equivalent power source model.

[0089] To statistically analyze the number of vehicles and their proportion in each region at any given time, a vehicle location indication function is defined, as shown in equation (6): (6); In formula (6): For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. For the car at the time t The regional status; k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index.

[0090] According to the definition in equation (6), the number and proportion of vehicles in the area can be calculated, as shown in equations (7) and (8) respectively: (7); (8); Upper Middle: Let be the number of vehicles in region k at time t; This represents the percentage of vehicles in the area. For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index; This represents the total number of electric vehicles included in the statistical sample.

[0091] The above results characterize the spatial distribution of vehicles over a 24-hour period, laying the foundation for calculating the available power of the electric vehicle group. In power aggregation calculations, not all vehicles in stationary areas can immediately participate in V2G discharge. Discharge availability is constrained by the battery's state of charge (SOC). Let the upper and lower SOC thresholds be... Y 1 and Y 2, then the first j The car at any time t The discharge state function is defined as shown in equation (9): (9); In equation (9): It is the discharge state function; For the first j The state of charge of the vehicle; and These are the lower discharge threshold and the upper charge threshold, respectively, satisfying... .when At that time, the vehicle is allowed to discharge; when At this time, charging is only allowed; when When the value is between the two thresholds, the vehicle is in standby mode and is not included in the discharge set.

[0092] Combining equations (6) and (9), the region can be calculated. k At any moment t The number of vehicles that can participate in the discharge is shown in equation (10): (10); In formula (10): For the region k At any moment t Number of vehicles capable of discharging electricity; For the region indicator function, indicating the first j The car at any time t Is it stationary and located in the area? k ; Let {1} be the discharge state function; let {1} be the indicator function, if and only if the vehicle satisfies the discharge availability condition of equation (9). The value is 1 when ) This represents the total number of vehicles included in the statistics.

[0093] To further consider the randomness of vehicle participation in V2G behavior, a Bernoulli random variable is introduced. This indicates whether the vehicle participates in the actual discharge. It follows the parameter... The Bernoulli distribution is shown in equation (11): (11); In equation (11): Indicates the time of the j-th car t Whether to participate in the discharge (1 for participation, 0 for non-participation); The average participation rate of the group reflects the probability that a vehicle will participate in V2G when it is in a state where it can be discharged.

[0094] Based on equation (11), the expected number of vehicles that can be discharged at a given time in the region can be calculated, as shown in equation (12): (12); In equation (12): For expectation operators; Indicates the proportion of group discharge participation; Indicates the region k At any moment t The number of vehicles; For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. Indicates the time of the j-th car t Whether to participate in the discharge (1 for participation, 0 for non-participation); Indicates the number of vehicles that can participate in the discharge; k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index; This represents the total number of electric vehicles included in the statistical sample.

[0095] This formula shows that under large sample conditions ( The average effect of random participation in a group is equivalent to that of proportional participation. Scaling the number of vehicles occupying a region provides a resolvable input for subsequent power aggregation.

[0096] Once the number of vehicles capable of discharging is determined, power layer aggregation can be further performed. Let the rated discharge power of a single vehicle be... Then the region k At any moment t The instantaneous equivalent output power is shown in equation (13): (13); In equation (13): For the region k The equivalent discharge power; This refers to the rated discharge power of a single vehicle. This represents the total number of electric vehicles included in the statistical sample. For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. Indicates the time of the j-th car t Whether to participate in the discharge (1 for participation, 0 for non-participation); Indicates the proportion of group discharge participation; Indicates the area k At any moment t Number of vehicles; approximation symbol " "This means that, in the sense of the population average, the expected value is used to replace the fluctuations caused by sample randomness.

[0097] At the population average level, taking the mathematical expectation of equation (13), we can obtain the expected form of regional power as shown in equation (14): (14); In equation (14): For the region at time t k The average equivalent power; For at any time t area k The mathematical expectation of the total number of vehicles in the system; Indicates the proportion of group discharge participation; This refers to the rated discharge power of a single vehicle. This represents the total number of electric vehicles included in the statistical sample. This represents the percentage of vehicles in the area.

[0098] Finally, by superimposing the power of the three types of areas (residential area, work area, and shopping area), the total equivalent output power of the system can be obtained, as shown in equation (15): (15); In equation (15): For the system at time t Total equivalent output power; For the region k The equivalent discharge power; k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index.

[0099] Based on the power aggregation model constructed by equations (7) to (15), the vehicle distribution and discharge capacity of residential areas, work areas, and shopping areas under four typical time-series scenarios—summer, winter, holidays, and weekdays—can be calculated to obtain the results. Figures 5-8 The diagram shows the equivalent power output curves of electric vehicles in residential, work, and shopping areas under four typical time-series scenarios. Figures 5-8 It can be seen that: the power output of residential areas is most stable at night, with continuous power supply capability from 22:00 to 6:00 the next day; the power output of work areas shows a bi-peak structure, corresponding to the morning and evening commuting peaks; the power output of shopping areas is concentrated between 16:00 and 20:00, reflecting the short-term response characteristics of commercial activities. Seasonal comparison shows that the overall power output is higher in summer than in winter, and the pace of travel is more moderate; in winter, vehicles stay in residential areas for a longer period of time, and charging and discharging activities are concentrated at night. In terms of daytime differences, the activity of shopping areas increases significantly during holidays, and the output curves of residential areas and shopping areas tend to be synchronized; while on weekdays, the power curves of residential areas and work areas show a significant complementary relationship, forming a temporal energy coordination and balance.

[0100] This section establishes an EV equivalent power supply model with multiple scenarios, regions, and time sequences, providing time-varying energy boundary conditions for the construction of the load power loss assessment system in the subsequent section 2, and also laying the foundation for the optimization of the power supply restoration strategy of multi-time sequence electric vehicle collaborative emergency power supply vehicle in the third section.

[0101] (II) Multi-regional load importance assessment and power outage loss modeling: Load loss refers to the various economic and social losses caused by the inability of a load to operate normally during the period from the occurrence of a power outage to the restoration of power supply. To quantitatively characterize the differences in the impact of loads in different areas during the power outage process, this section first constructs a multi-dimensional load importance index system from three aspects: life safety, economic benefits, and social operation. For typical load scenarios, this invention takes an improved IEEE 33-node distribution network system as the research object. The example area is divided into three functional areas: residential area, work area, and shopping area. Power outage load points are set at nodes 21, 14, and 5. The network topology is as follows: Figure 9 As shown. Assuming a sudden power outage occurs at a specific time (12:00), the set of load importance factors is set as ε. i Furthermore, taking into account the differences in importance weights of different types of loads (residential area, work area, and shopping area loads), a calculation model for the load importance index is established, and its specific expression is shown in equation (16).

[0102] (16); In equation (16): This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. This is a safety amplification factor that only applies to the life / safety dimension; These are parameters for this type in terms of life / safety, economics, and special / social impact.

[0103] Equation (16) can be used to comprehensively measure the multidimensional sensitivity of load to safety, economy and society, and provide a priority quantitative basis for subsequent loss assessment.

[0104] To characterize the cumulative characteristics of economic losses during power outages over time, a load loss loss modeling system based on the time-rate-of-loss method is established. Let the rate of change of economic losses from load loss between adjacent time points be the "load loss rate of increase," and its calculation method is shown in equation (17): (17); In equation (17): For load type k In the interval Internal loss growth rate (unit: yuan / kW·min); For load type k The cumulative cost curve of nodes (RMB / kW); t s , t s+1 These represent adjacent calculation times; s For the first s Indexes for time intervals.

[0105] Equation (17) provides the rate of increase of load loss over time, laying the foundation for subsequent cumulative loss calculations. To further reflect the role of different types of load capacity in the loss, a weighted model is introduced for comprehensive calculation, as shown in Equation (18): (18); In equation (18): For load type k Duration of the power outage d Cumulative losses within the period (unit: yuan); For type k Load capacity; This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. For load type k In the interval Internal loss growth rate (unit: yuan / kW·min); Representing the interval [0,d] and the segment Effective overlap duration; s For the first s Indexes for time intervals.

[0106]

[0107]

[0108]

[0109] Based on the multi-area load importance and power outage loss model established by equations (16) to (18), and combined with the improved load composition structure of three typical areas (residential area, work area, and shopping area) in the IEEE 33-node system, according to the cumulative cost of various load nodes given in Table 1, the importance parameters for various loads given in Table 2, and the typical load capacity values ​​of the three types of areas given in Table 3, the power outage loss of the residential area, work area, and shopping area under different power outage durations is calculated piecewise. This allows for the plotting of... Figure 10 The graphs show the changes in load loss as a function of power outage duration for three typical areas (residential area, workplace, and shopping area).

[0110] from Figure 10It can be seen that: the power loss curve of residential area load increases relatively slowly during the short-term power outage phase, indicating that residential loads have a certain tolerance to short-term power interruptions; the loss curve of workplace load increases approximately linearly with the power outage time, reflecting the continuous accumulation of losses after the interruption of production and business activities; the loss curve of shopping area load rises sharply in the initial stage of the power outage, indicating that even a short-term power outage can lead to high economic losses. When the power outage duration exceeds about 500 minutes, the curve increase gradually slows down, but the absolute loss level is significantly higher than that of the other two types of areas, indicating that commercial activities are most dependent on the continuity of power.

[0111] In summary, the load importance index system and time-rate loss model established in this section can dynamically reflect the differences in power outage duration, capacity, and importance of different loads, providing a scientific and quantifiable basis for subsequent power optimization allocation and rapid recovery strategies.

[0112] (III) Multi-time sequence power restoration strategy for electric vehicles (EVs) only: The EV-only multi-time-series power restoration strategy first constructs an optimized power restoration model for EVs with only electric vehicles involved, based on the aforementioned multi-time-series EV equivalent power source model and load power loss assessment system. A genetic algorithm (GA) is then used to optimize the power allocation of EVs in the time domain and between nodes. Based on this, an improved IEEE 33-node distribution network system is selected as a case study for typical load scenarios. The study area is divided into three functional areas: residential, work, and shopping. Power loss load points are set at nodes 21, 14, and 5. The network topology is as follows: Figure 9 As shown. Assume that a sudden power outage occurs at a specific time (12:00), and in the initial stage of the power outage, local rapid power restoration is achieved solely by electric vehicles that are already connected to the power distribution network.

[0113] In the above scenario, the economic cost of the system recovery process mainly comes from three parts: switching operation cost, mobile power supply usage cost, and load power loss loss. The switching operation cost S is used to describe the operational cost of the system during network reconstruction; the mobile power supply usage cost is used to quantify the comprehensive cost incurred during the discharge of electric vehicles (EVs) and the replenishment and maintenance of emergency power supply vehicles (EPSVs), and is treated as a unified cost item in the model; the load power loss loss reflects the economic and social impact of the load during the period from power outage to restoration.

[0114] Since different nodes are located at different distances from the power source, the cost is positively correlated with the distance, as shown in equation (19): (19); In equation (19): α is the operating cost coefficient corresponding to a unit electrical distance, which is used to characterize the increased operating cost for each additional unit electrical distance; For the first j The electrical distance between the power source of a switch and the power receiving load can be the impedance distance, line length, or distance converted based on the equivalent voltage level. For the first j The cost of a single operation of a switch is used to quantify the economic cost of that switch during fault isolation and power restoration.

[0115] After obtaining the operating cost of a single switch, assume there are a total of [costs] during the recovery period. If there are multiple operating switches, the total cost of switch operation can be expressed as shown in equation (20): (20); In equation (20): S Total cost of switch operation; n s Number of operating switches; For the first j The cost of a single operation of a switch.

[0116] In addition to considering the switching operation costs, it is also necessary to characterize the comprehensive costs incurred during the discharge of electric vehicles (EVs) and the replenishment and maintenance of emergency power supply vehicles (EPSVs). The cost of using a mobile power source consists of the costs of conventional use. With compensation costs It consists of two parts, and the conventional usage cost is shown in equation (21) below: (twenty one); In equation (21): C 1 represents the typical operating cost of a mobile power bank collection (EV and EPSV) over a single power supply cycle; W The cost of unit equivalent electrical energy (RMB / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M The unified operation and maintenance costs for EVs and EPSVs.

[0117] If some mobile power sources need to be replenished with energy during the later stages of recovery (grid power replenishment for EVs and fuel supply for EPSVs), the compensation cost is shown in equation (22): (twenty two); In equation (22): C 2 represents the compensation costs incurred by the mobile power bank collection (EV and EPSV) in restoring usable capacity after a power outage event; WThe cost of unit equivalent electrical energy (RMB / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 represents the EV discharge electricity price or EPSV fuel conversion cost.

[0118] In normal usage costs C 1 and compensation costs C Based on the given information, the total cost of using the mobile power supply set (EV and EPSV) in a single power supply cycle can be expressed as the sum of the two, as shown in equation (23): (twenty three); In equation (23): The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle; The typical operating cost of a mobile power bank collection (EV and EPSV) over a single power supply cycle; Compensation costs incurred by mobile power bank collections (EVs and EPSVs) to restore available capacity after a power outage event; W The cost of unit equivalent electrical energy (RMB / kWh) takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M The unified operation and maintenance costs for EVs and EPSVs.

[0119] Besides the switching operation costs and the cost of using a mobile power supply, the economic losses caused by the load's inability to obtain normal power supply during a power outage are also significant. The power deficit of the system at any given time can be represented by the difference between the demand power and the actual power supplied, as shown in equation (24): (twenty four); In equation (24): For the system at time t The power gap represents the unmet load demand at that moment; For the region k At any moment t The load demand power; The available power provided by the EV vehicle.

[0120] To convert the power deficit into an economic loss rate, a loss growth rate function is introduced. This reflects the economic loss caused by a unit power shortage per unit time. The system at time... t The economic loss rate can be defined as shown in equation (25): (25); In equation (25): For the system at time t Economic loss rate (unit: yuan / min); For the system at time t The power gap represents the unmet load demand at that moment; It is the loss growth rate function of the system at time t (unit: yuan / (min·kW)), used to quantify the sensitivity of the power outage duration to economic losses, such as the impact of peak hours on critical loads. The load will be significantly higher than during normal or off-peak periods, and its value can be determined by the segmented increment rate for each load type. The weighted average is obtained.

[0121] Integrating the instantaneous loss rate over the entire power outage period yields the cumulative economic loss of the system during the power outage event, as shown in equation (26): (26); In equation (26): For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; T This represents the maximum duration of this power outage event; For the system at time t The rate of economic loss.

[0122] After comprehensively considering the economic losses from the outage, the switching operation costs, and the V2G usage costs of electric vehicles, the overall power loss loss of this power outage event can be uniformly expressed as shown in equation (27): (27); In equation (27): The total power loss of the system during a single power outage-recovery process; For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; S Total cost of switch operation; The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle.

[0123] Based on the optimal time-space scheduling strategy obtained through GA convergence, a comprehensive node importance weight is introduced to reflect the differences in reliability and economy among different nodes. and node economic loss growth rate Weighting the power deficit, the overall power loss of the system is expressed as shown in equation (28): (28); In equation (28): This refers to the total power loss of the system when only electric vehicles participate in power restoration; Assuming a comprehensive importance weight for each node, Increased rate of economic loss at nodes; and They are nodes at time 10:00 and 20:00 respectively. t Demand power and recovery power; S Total cost of switch operation; The total cost of using a collection of mobile power supplies (EVs and EPSVs) over a single power cycle.

[0124] Based on this, a comprehensive optimization objective function for the EV scenario only is constructed, as shown in equation (29): (29); In equation (29): This is the comprehensive objective function value in the EV scenario only, and it is also the evaluation index of individual fitness in GA (the smaller the value, the better the solution). For the first j Power supply to load node a The shortest electrical distance (km); For nodes j The power supply capacity (kW); This refers to the total power loss of the system when only electric vehicles participate in power restoration; , These are the weighting coefficients for distance cost and power outage loss, respectively.

[0125] To ensure that the solution results satisfy the physical and operational constraints, the power allocation of EV also needs to be constrained during the GA iteration process. The typical constraint can be expressed as shown in equation (30): (30); In equation (30): For the first j Vehicles towards the area k Power allocation ratio coefficient of power supply; The available power provided by the EV vehicle; For the region k The load demand power. The first constraint states that the sum of the vehicle power allocation ratio coefficients shall not exceed 1 to ensure the vehicle power supply capacity constraint; the second constraint states that the node recovery power shall not be negative, nor shall it exceed 1.1 times the demand power to ensure system voltage stability.

[0126]

[0127] Based on the above-mentioned EV power supply recovery model and GA optimization process, combined with the load power loss assessment results and equations (28) to (30), simulation calculations were carried out on four typical time-series scenarios (summer, winter, weekday, and holiday) and three types of areas (residential area, work area, and shopping area). The node power allocation results of EVs in each scenario can be obtained, and the EV allocation details are summarized in Table 1. Table 1 shows the statistical results of EV power supply allocation and utilization rate in different scenarios, different areas, and key nodes. As can be seen from Table 1: in the night and winter scenarios, the residential area node receives the highest proportion of EV power support, indicating that EVs mainly form stable energy storage support in the residential area; during the daytime hours of weekdays, some EV power is tilted towards the work area node, reflecting the priority recovery demand of the daytime office load; in the holiday scenario, the proportion of EV power supply in the shopping area node is significantly increased, which is consistent with the characteristics of active shopping travel during holidays in the multi-time-series travel model described in Section 1.

[0128] (iv) Modeling of EV-EPSV Coordinated Power Supply Restoration Strategy: This step further introduces emergency power supply vehicles (EPSVs) to participate in power supply scheduling, forming a collaborative optimization strategy of "EV static allocation + EPSV dynamic compensation". In the first stage, the Genetic Algorithm (GA) completes the initial power and node allocation of EVs; in the second stage, the Adaptive Large Neighborhood Search (ALNS) algorithm is introduced to dynamically optimize the driving path and stopping order of EPSVs.

[0129] Based on this, an improved IEEE 33-node distribution network was selected as the case study system. The network was divided into three areas: residential, work, and shopping. Power outage load points were set at nodes 21, 14, and 5, and EPSV sites were deployed at nodes 20, 15, and 8, with 30 emergency power supply vehicles configured. Assuming a sudden power outage occurs at 12:00, the EV (Electric Power Vehicle) first achieves rapid local restoration according to the GA (General Aspect Ratio) optimization results, followed by EPSV sequentially connecting for compensation according to the GA-ALNS two-stage optimization results. The network topology is as follows: Figure 9 As shown, the two-stage optimization process of the multi-time-domain EV-EPSV collaborative power supply recovery strategy is as follows: Figure 17 As shown.

[0130] Based on this, the EV group provides initial support, while EPSV provides mobile support to key nodes. Together, they form a regional coordinated power supply capability, as shown in equation (31): (31); In equation (31): For the region k At any moment tThe equivalent combined power supply; Power allocation factor; For nodes j The equivalent power; For emergency vehicles v To load k Power supply capacity.

[0131] To simplify subsequent derivations, EV and EPSV are compared to the region. k The provided power is denoted as follows: and The equivalent representation of the combined power supply is obtained, as shown in equation (32): (32); In equation (32): For the region k At any moment t Combined power supply of EV-EPSV; For the region k At any moment t Power supplied by the EV group; For the region k At any moment t Power supplied by EPSV.

[0132] Since the importance and load scale of nodes vary in different regions, a regional weighting coefficient is introduced to reflect this unevenness in the system evaluation. The region weight is defined as shown in equation (33): (33); In equation (33): For the region k The weighted importance coefficient comprehensively reflects the load scale and the weight of key nodes in the region; For the region k Set of internal nodes; For nodes i Importance weight; For nodes i The required power.

[0133] To maintain symbol consistency, the region k The total power demand is denoted as Its definition is shown in equation (34): (34); In equation (34): For the region k At any moment t The required power; For the region k At any momentt The load demand power.

[0134] Based on the above definitions, and considering the differences in load scale and critical node distribution across different regions, a regional weighting coefficient is introduced. Weighting the power deficit, the overall power loss of the system under the EV-EPSV collaborative recovery scheme is shown in equation (35): (35); In equation (35): The overall power loss loss under the EV and EPSV collaborative power supply scheme; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply for EV and EPSV; S Total cost of switch operation; Let $\frac{EV}{EPSV}$ be the total cost of using the mobile power supply set (EV and EPSV) in a single power supply cycle. Considering the balance between power supply resource fairness and economic efficiency, a corresponding comprehensive optimization objective function is constructed. The comprehensive optimization objective in the EV-EPSV collaborative scenario can then be expressed as shown in equation (36): (36); In equation (36): The comprehensive objective function value under the EV–EPSV collaborative power supply scheme is the fitness evaluation index in the ALNS search process; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply of the EV and EPSV; λ is the weighting coefficient; The proportion of power supplied to the target node; The total nominal power supply capacity (maximum power supply capacity) available for the EV-EPSV vehicle group during the cooperative power supply phase. The first item represents the residual load loss item; the second item represents the fairness penalty item.

[0135] To ensure that the optimization results are physically and operationally feasible, it is also necessary to introduce constraints on the EPSV output and service relationship, as shown in equation (37): (37); In equation (37): The combined power supply for EV and EPSV; For the region kAt any moment t The required power; For emergency vehicles v To load k Power supplied; This represents the vehicle's maximum output capacity. This is a binary variable indicating whether the vehicle is enabled (1 indicates enabled, 0 indicates disabled); Constraint variables are assigned to the power supply task. The first constraint states that the combined power supply power of the region cannot be negative and cannot exceed the power demand of the region, so as to ensure that the power supply level of each region is physically feasible and meets the basic supply and demand balance constraint. The second constraint states that the power provided by the emergency power supply vehicle to the region shall not exceed the vehicle's maximum output capacity and shall be consistent with the start-stop state variable, so as to ensure that the output of a single vehicle does not exceed the limit and conforms to the vehicle's activation state. The third constraint states that the sum of the service allocation variables of each region at the same time does not exceed 1, so as to ensure that each region is served by at most one emergency power supply vehicle, thereby ensuring that task allocation and route scheduling are feasible in actual operation.

[0136]

[0137] Based on the EV-EPSV collaborative optimization model constructed using equations (31) to (37), and the static allocation results of EVs provided by GA, the paths and power of EPSVs are dynamically reconstructed by ALNS. By jointly solving multiple scenarios (summer, winter, weekdays, holidays) and multiple regions (residential areas, work areas, shopping areas), the departure time, service order, and node power supply of each emergency power supply vehicle can be obtained as scheduling schemes. By statistically summarizing the EPSV scheduling results under each scenario, the EPSV allocation summary table shown in Table 2 can be obtained.

[0138] As shown in Table 2, under the collaborative power supply scheme, the residential area receives a higher proportion of EPSV supplementary power in all four typical time-series scenarios, reflecting the priority of this area in terms of life safety and basic living security; the working area sees a significant increase in the proportion of EPSV input on weekdays, consistent with its characteristics of high daytime load and high sensitivity to production activities; the shopping area receives more EPSV support during holidays, reflecting the high economic loss risk corresponding to commercial load during holidays. Overall, the collaborative optimization model constructed by equations (31) to (37), solved by the GA–ALNS two-stage algorithm, enables EV and EPSV to achieve a relatively balanced and coordinated resource allocation in the time and space dimensions, laying a quantitative foundation for the comparative analysis of power loss and saving rate between the EV-only scheme and the collaborative scheme in the next section.

[0139] (V) Comparative analysis of the EV-only solution and the EV-EPSV collaborative solution: To quantitatively evaluate the economic differences and recovery effects of the EV-only power supply scheme and the EV-EPSV collaborative power supply scheme under different time and space scenarios, this section compares and analyzes four typical time-series scenarios (summer, winter, weekday, and holiday) and three typical areas (residential area, work area, and shopping area) based on the power loss model (28) of the EV-only scheme in Section 3 and the power loss model (35) of the collaborative scheme in Section 4.

[0140] In scenarios where only EVs participate in power supply, a comprehensive node importance weight is introduced to reflect the differences in node importance and loss sensitivity. and node economic loss growth rate Weighting the power deficit, the overall power loss of the system is expressed as shown in equation (28): (28); In equation (28): This refers to the total power loss of the system when only electric vehicles participate in power restoration; Assuming a comprehensive importance weight for each node, Increased rate of economic loss at nodes; and They are nodes at time 10:00 and 20:00 respectively. t The required power and recovery power; S Total cost of switch operation; The total cost of using an electric vehicle over a complete V2G cycle.

[0141] Based on the above definitions, and considering the differences in load scale and critical node distribution across different regions, a regional weighting coefficient is introduced. Weighting the power deficit, the overall power loss of the system under the EV-EPSV collaborative recovery scheme is shown in equation (35): (35); In equation (35): The overall power loss loss under the EV and EPSV collaborative power supply scheme; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply for EV and EPSV; S Total cost of switch operation; The total cost of using an electric vehicle over a complete V2G cycle.

[0142] Based on equations (28) and (35), a comparison chart of the power loss between the collaborative power restoration strategy of this invention and the traditional EV-only power supply scheme can be obtained in four typical time-series scenarios (summer, winter, weekday, and holiday) and three types of areas (residential area, workplace, and shopping area). Taking the summer and weekday scenarios as examples, the following is obtained: Figures 11-16 .Depend on Figures 11-16 It can be seen that in the initial stage of the power outage, the loss curves of the two schemes start at similar points, indicating that the rapid grid-connected discharge of the EV group can provide initial support to the load in a short period of time. However, as time goes on, the curve of the EV-only scheme rises significantly in the middle and late stages due to limited energy storage capacity and spatial distribution, showing power decay and recovery lag. In contrast, the curve of the collaborative scheme is significantly lower than that of the EV-only scheme and is smoother overall. This indicates that EPSV effectively makes up for the power supply gap after the EV energy release through cross-regional mobile energy replenishment in the middle and late stages, making the power supply recovery process more continuous and stable, and significantly improving the system's dynamic response capability and overall power supply reliability.

[0143] In addition to comparing cumulative losses, a relative savings rate index is introduced to quantify the economic differences between the two strategies across multiple scenarios (summer, winter, holidays, weekdays) and multiple regions (residential areas, work areas, commercial areas). As shown in equation (38): (38); In equation (38): Indicates in the scene s area a The relative savings rate of the EV-EPSV collaborative power supply solution compared to the EV-only solution. The larger the value, the more obvious the economic advantage of the collaborative solution in this spatiotemporal scenario; Indicates in the scene s area a The overall power loss of the EV solution only; Indicates in the scene s area a Overall power loss of the EV-EPSV collaborative scheme; s Indicates the scene type (summer, winter, weekday, holiday); a Indicates the area category (residential area, work area, shopping area).

[0144]

[0145] Based on formula (38) Figures 11-16The cumulative power loss in each scenario and region was normalized, resulting in Table 3, which shows the power loss saving rate for each scenario and region. The results show that the collaborative strategy achieved positive savings in all cases, with the overall saving rate ranging from 34% to 70%. From a regional perspective, residential areas showed the most significant savings, with an average saving rate of approximately 60%–70%, indicating that under the condition of "concentrated nighttime travel and dispersed daytime travel" in residential areas, the stable nighttime presence of EVs and cross-timetime refueling by EPSVs formed a good synergy. Work areas had a lower saving rate during holidays (approximately 34%), rising to approximately 50% on weekdays, reflecting that the concentrated vehicle traffic and rigid load characteristics of work areas on weekdays are more conducive to the effectiveness of collaborative scheduling. Shopping areas performed best under holiday conditions, with a saving rate of approximately 60%, indicating that when commercial load increases significantly during holidays, the joint scheduling of EVs and EPSVs can effectively reduce the high losses caused by the interruption of high-value commercial activities.

[0146] From a scenario perspective, the savings rate in summer and winter is generally similar, with summer slightly better than winter. This is mainly because the load base and adjustable energy storage are both higher in summer, allowing for greater flexibility in peak load reduction and continuous energy supply through coordinated scheduling. Holidays are generally better than weekdays, which is related to increased activity in shopping areas and more dispersed EV spatial distribution, making the mobility advantage of EPSVs more prominent at these times.

[0147] In summary, the comparative evaluation results based on equations (28), (35) and (38) show that, compared with the EV-only power supply scheme, the EV-EPSV collaborative power supply recovery strategy proposed in this invention can significantly reduce system power loss, improve recovery speed and power supply continuity under different time scenarios and regional types, and take into account both economy and reliability. This verifies the effectiveness and practical value of the aforementioned multi-time-sequence EV equivalent power supply model, load power loss assessment system and EV-EPSV two-stage collaborative optimization framework.

Claims

1. A power supply restoration strategy for electric vehicles in collaboration with the power grid, considering multi-time-domain EV-EPSV, characterized in that... Includes the following steps: Step 1: In the event of a power grid failure, establish a multi-time-series electric vehicle (EV) equivalent power source model based on the travel patterns of EV users in typical travel scenarios; Step 2: Construct a load importance index system, and combine it with load capacity and outage duration to establish a load power loss model, so as to realize the quantitative calculation of economic and social losses of different types of loads in multi-sequence power outage process; Step 3: Construct an optimized power supply model for the collaboration between electric vehicles (EVs) and emergency power supply vehicles (EPSVs) to achieve static power allocation for EVs and dynamic path and power optimization scheduling for emergency power supply vehicles.

2. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 1, characterized in that: In step 1, based on the dynamic evolution characteristics of electric vehicle user travel patterns and combined with NHTS2017 travel sample data, the data is divided into summer and winter scenarios by season and weekday and holiday scenarios by time sequence to complete data fitting. Then, a vehicle travel state transition matrix is ​​established through a semi-Markov chain, and the vehicle behavior is stochastically simulated using the Monte Carlo method to obtain the available discharge capacity of electric vehicles at each time. Thus, the electric vehicle cluster in the region is equivalent to a time-varying power source model, and a multi-time-series EV equivalent power source model is established.

3. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 2, characterized in that: In step 1, in order to characterize the travel patterns of electric vehicle users in different seasons and time periods, the travel sample data of NHTS2017 is first divided into summer and winter scenarios according to season, and into weekday and holiday scenarios according to time period, thus forming four typical travel scenarios. For each typical travel scenario, samples of departure and arrival times of electric vehicle users are extracted, and a weighted model is performed using a wrapped Gaussian mixture model. The probability density function is shown in equation (1): （1）； In formula (1): For the scene s ,area k Behavioral types q At any moment t The probability density function; Indicates in the scene s ,area k Behavioral types q The number of Gaussian components used below; The weights of each component satisfy the following conditions: , ; and These represent the center and standard deviation of the component, respectively. To wrap around the Gaussian kernel, so as to achieve 24h periodization and ensure the continuity and normalization of the distribution in the interval [0,24); t This indicates the corresponding intraday time, with a value range of 0–24 hours. s This indicates four typical operating scenarios: summer, winter, weekdays, and holidays. k This represents three typical areas: residential areas, work areas, and shopping areas. q This indicates the behavior type, used to distinguish between two types of electric vehicle usage behaviors: departure and arrival; In Gaussian mixtures, the first... c The sequence number of each Gaussian component; To further avoid the distortion of the distribution of electric vehicle departure / arrival times at the 24-hour boundary and to maintain periodic consistency, the wrap-around Gaussian kernel function is defined as shown in equation (2): （2）； In formula (2): This is a wrapping Gaussian kernel function with a period of 24 hours; t It is a time variable; μ , σ These are the mean and standard deviation, respectively. m This is a 24-hour shift index used for periodic extension of the time axis; Represents a set of integers; When the sample size is small or there is morphological skew, the periodic kernel density estimation method is used for smooth estimation, and its expression is shown in equation (3): （3）； In formula (3): In the scene s ,area k Behavioral types q The periodic kernel density function; This represents the total number of samples used for periodic kernel density estimation; h For bandwidth parameters; Indicates the first i Each sample time point; m ∈{−1,0,1} is a periodic translation term used to smoothly connect at the boundary [0,24]. This represents an exponential function with base e; By using equations (1) to (3), and through joint modeling of the wrapped Gaussian mixture model and the periodic kernel density estimation, a time distribution function that is both smooth and consistent with the 24-hour period can be obtained under different sample sizes and morphological conditions.

4. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 3, characterized in that: After completing the data fitting, a mathematical model of multi-time series electric vehicle travel behavior was established based on the vehicle transfer kernel matrix of the semi-Markov chain and the Monte Carlo method. First, the time-related transition probabilities of a vehicle between three areas—a residential area, a work area, and a shopping area—are described by a semi-Markov chain. The definition of the vehicle's semi-Markov kernel matrix is ​​shown in equation (4): （4）； In equation (4): For a moment t The semi-Markov transition kernel matrix; Indicates that electric vehicles are in the area m The stay time shall not exceed t In the case of transferring to the region n The probability, it also characterizes the probability from m arrive n The transition probability and the residence time distribution in this state, where, m , n ∈{1,2,3}, areas 1 / 2 / 3 correspond to residential area / work area / shopping area respectively; After obtaining the transition kernel matrix, in order to obtain the group-level time-series trajectory samples, the Monte Carlo method is used to randomly sample and evolve the vehicles' states. The vehicle state matrix within 24 hours of a day is defined, and its form is shown in Equation (5): （5）； In formula (5): For the vehicle state matrix; Indicates the first j electric vehicles at all times t The area i ,in, i =1,2,3: This represents the area code, where 1 represents a residential area, 2 represents a work area, and 3 represents a shopping area. t =1,2,…,24: represents the 24 hourly times of the day; j Indicates the first j A car, j =1,2,…, , This indicates the total number of electric vehicles included in the statistical sample. The semi-Markov kernel matrix of the vehicle given by equation (4) provides probability-driven propagation, while the vehicle state matrix given by equation (5) carries the temporal distribution of the group obtained by Monte Carlo sampling. Together, they constitute the core input and output of the multi-temporal electric vehicle travel chain model and provide a complete spatiotemporal statistical basis for the equivalent power source model.

5. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 4, characterized in that: Based on this vehicle state matrix The number of vehicles and their proportion in each region at any given time are statistically analyzed, and then the available power model of the electric vehicle group is constructed, as shown in equation (6): （6）； In formula (6): For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. For the car at the time t The regional status; k ∈{1,2,3} correspond to the residential area, the work area, and the shopping area, respectively; t ∈{1,2,…,24} is the time index; According to the definition in equation (6), the number and proportion of vehicles in the area are calculated as shown in equations (7) and (8), respectively: （7）； （8）； In the above formula: Let be the number of vehicles in region k at time t; This represents the percentage of vehicles in the area. For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. This represents the total number of electric vehicles included in the statistical sample. Discharge availability is constrained by the battery's state of charge (SOC); assuming The upper and lower limits of SOC are respectively Y 1 and Y 2, then the first j The car at any time t The discharge state function is defined as shown in equation (9): （9）； In equation (9): It is the discharge state function; For the first j The state of charge of the vehicle; and These are the lower discharge threshold and the upper charge threshold, respectively, satisfying... ;when At that time, the vehicle is allowed to discharge; when At this time, charging is only allowed; when When the value is between the two thresholds, the vehicle is in standby mode and is not included in the discharge set. Combining equations (6) and (9), calculate the region. k At any moment t The number of vehicles that can participate in the discharge is shown in equation (10): （10）； In formula (10): For the region k At any moment t Number of vehicles capable of discharging electricity; For the region indicator function, indicating the first j The car at any time t Is it stationary and located in the area? k ; Let {1} be the discharge state function; let {1} be the indicator function, if and only if the vehicle satisfies the discharge availability condition of equation (9). The value is 1 at this time. To further consider the randomness of vehicle participation in V2G behavior, a Bernoulli random variable is introduced. This indicates whether the vehicle participated in actual discharge; Its parameters are The Bernoulli distribution is shown in equation (11): （11）； In equation (11): Indicates the time of the j-th car t Whether or not it participates in the discharge: 1 for participation, 0 for non-participation; The average participation rate of the group reflects the probability that a vehicle will participate in V2G when it is in a state where it can be discharged. Represents a Bernoulli distribution, and outputs a random variable of 0–1; Based on equation (11), the expected number of vehicles that can be discharged at a given time in the region is calculated, as shown in equation (12): （12）； In equation (12): For expectation operators; Indicates the proportion of group discharge participation; Indicates the region k At any moment t The number of vehicles; For the first j electric vehicles at the time t Is it located in the area? k The indicator variable, if and only if the first j The car in t Hours belong to the area k The value is 1 if the condition is met, otherwise it is 0. Indicates the time of the j-th car t Whether or not it participates in the discharge: 1 for participation, 0 for non-participation; Once the number of vehicles capable of discharging is obtained, power layer aggregation is further performed; assuming the rated discharge power of a single vehicle is... Then the region k At any moment t The instantaneous equivalent output power is shown in equation (13): （13）； In equation (13): For the region k The equivalent discharge power; This refers to the rated discharge power of a single vehicle. The total number of electric vehicles included in the statistical sample; approximation symbol " "This means that, in the sense of the population average, the expected value is used to replace the fluctuations caused by sample randomness; At the population average level, taking the mathematical expectation of equation (13), we can obtain the expected form of regional power as shown in equation (14): （14）； In equation (14): For the region at time t k The average equivalent power; For at any time t area k The mathematical expectation of the total number of vehicles in the system; This represents the percentage of vehicles in the area. Finally, by superimposing the power of the three types of areas—residential area, work area, and shopping area—the total equivalent output power of the system can be obtained, as shown in equation (15): （15）； In equation (15): For the system at time t Total equivalent output power; By combining the vehicle's travel chain, SOC constraint, participation probability and power aggregation using formulas (1) to (15), a multi-temporal electric vehicle (EV) equivalent power model that can describe the temporal behavior characteristics of the group was established.

6. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 5, characterized in that: In step 2, firstly, a load importance index system is established, dividing load importance into three categories: Life / Safety Loss Parameters This is used to describe loads such as medical care and emergency support that have a critical impact on personal safety; Economic loss parameters This reflects the economic losses caused by power outages to enterprise production and business activities; Special / Social Impact Parameters It is used to characterize the loads of public services, transportation facilities, and other facilities that are of particular importance to the operation of society; Set load type k ={Residence, Work, Shopping}; Scenarios s ={Summer, Winter, Weekdays, Holidays}; Importance parameter vectors are set for various load types. And introduce a safety amplification factor that only applies to the life / safety dimension. Based on this definition, the key parameters are as shown in equation (16): （16）； In equation (16): This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. This is a safety amplification factor that only applies to the life / safety dimension; These are parameters for this type in terms of life / safety, economics, and special / social impact.

7. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 6, characterized in that: To characterize the cumulative economic losses of loads during power outages over time, a load loss loss modeling system based on the time-increase rate method is established, specifically including: Let the rate of change of economic loss due to load power loss between adjacent time nodes be the load loss growth rate, and its calculation method is shown in Equation (17): （17）； In equation (17): For load type k In the interval Internal loss growth rate; Indicates at time Time type k The cumulative cost of load loss; Indicates at time Time type k The cumulative cost of load loss; t s , t s+1 These represent adjacent calculation times; s For the first s Indexes for time intervals; To further reflect the role of different types of load capacity in losses, a load importance weighting method is introduced for comprehensive calculation, as shown in equation (18): （18）； In equation (18): For load type k Duration of the power outage d The cumulative losses within; For type k Load capacity; This is the overall importance coefficient of the load; the larger the value, the higher the priority of this type of load in resource security and recovery. For load type k In the interval Internal loss growth rate; Representing the interval [0,d] and the segment Effective overlap duration; This indicates the number of interval segments, which means dividing the power outage process into 7 time periods for segmented accumulation of losses.

8. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 7, characterized in that: In step 2, the cost composition of the system recovery process is first uniformly modeled; the comprehensive economic cost of the system recovery process mainly consists of three parts: switching operation cost S, mobile power supply usage cost, and load power loss loss; The switching operation cost S is used to describe the operational cost of the system during network reconfiguration; The cost of using mobile power supplies is used to quantify the comprehensive costs incurred during the discharge of electric vehicles and the replenishment and maintenance of emergency power supply vehicles, and is treated as a unified cost item in the model; Load loss reflects the economic and social impact of the load during the period from power outage to restoration; Since different nodes are located at varying distances from the power source, and cost is positively correlated with distance, the cost can be... j The operating cost of a switch is modeled as being proportional to the distance, as shown in equation (19): （19）； In equation (19): α is the operating cost coefficient corresponding to a unit electrical distance, which is used to characterize the increased operating cost for each additional unit electrical distance; For the first j The electrical distance between the power source of a switch and the electrical load can be the impedance distance, the line length, or the distance converted by the equivalent voltage level. For the first j The cost of a single operation of a switch is used to quantify the economic cost of the switch during fault isolation and power restoration. After obtaining the operating cost of a single switch, assume there are a total of [costs] during the recovery period. If there are multiple operating switches, the total cost of switch operation can be expressed as shown in equation (20): （20）； In equation (20): S Total cost of switch operation; n s Number of operating switches; For the first j The cost of a single switch operation; While considering the cost of switching operations, it is also necessary to consider the comprehensive costs incurred during the discharge of electric vehicles and the replenishment and maintenance of emergency power supply vehicles. The cost of using mobile power supplies is higher than the cost of regular use. With compensation costs It consists of two parts, and the conventional usage cost is shown in equation (21) below: （21）； In equation (21): C 1 represents the standard operating cost of a mobile power bank collection within a single power supply cycle; W The cost of unit equivalent electrical energy takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M To unify the operation and maintenance costs for EVs and EPSVs; If some mobile power sources need to be replenished with energy during the later stages of recovery, including grid power replenishment for EVs and fuel replenishment for EPSVs, the compensation cost is shown in equation (22): （22）； In equation (22): C 2 represents the compensation cost incurred by the mobile power bank collection in restoring usable capacity after a power outage event; W The cost of unit equivalent electrical energy takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; In normal usage costs C 1 and compensation costs C Based on the given information, the total cost of using the mobile power supply set in a single power supply cycle can be expressed as the sum of the two, as shown in equation (23): （23）； In equation (23): The total cost of using a collection of power banks within a single power cycle; The typical usage cost of a mobile power bank collection over a single power cycle; The compensation cost incurred by a mobile power bank collection in restoring available capacity after a power outage event; W The cost of unit equivalent electrical energy takes into account both the EV discharge settlement electricity price and the EPSV fuel consumption conversion value. D 1 indicates the EV discharge electricity price or EPSV fuel conversion cost; D 2 represents the cost equivalent to energy loss and lifespan depreciation; M To unify the operation and maintenance costs for EVs and EPSVs; In addition to the switching operation costs and the cost of using a mobile power supply, the economic losses caused by the load's inability to obtain normal power supply during a power outage are also not negligible; the power gap of the system at any given time is represented by the difference between the demand power and the actual power supplied, as shown in equation (24): （24）； In equation (24): For the system at time t The power gap represents the unmet load demand at that moment; For the region k At any moment t The load demand power; The available power provided by the EV vehicle; To convert the power deficit into an economic loss rate, a loss growth rate function is introduced. This reflects the economic loss caused by a unit power deficit per unit time; the system at time... t The economic loss rate can be defined as shown in equation (25): （25）； In equation (25): For the system at time t The rate of economic loss; For the system at time t The power gap represents the unmet load demand at that moment; It is the loss growth rate function of the system at time t, used to quantify the sensitivity of the power outage duration to economic losses; Integrating the instantaneous loss rate over the entire power outage period yields the cumulative economic loss of the system during the power outage event, as shown in equation (26): （26）； In equation (26): For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; T This represents the maximum duration of this power outage event; For the system at time t The rate of economic loss; After comprehensively considering the economic losses from the outage, the switching operation costs, and the V2G usage costs of electric vehicles, the overall power loss loss of this power outage event can be uniformly expressed as shown in equation (27): （27）； In equation (27): The total power loss of the system during a single power outage-recovery process; For the system during the power outage duration [0, T The total cumulative economic losses within [the specified range]; S Total cost of switch operation; The total cost of using a collection of power banks within a single power cycle.

9. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 8, characterized in that: In step 3, in the recovery scenario where only electric vehicles (EVs) participate in power supply, the genetic algorithm (GA) is used to search for the optimal spatiotemporal allocation strategy of electric vehicles (EVs) within a 24-hour period. The overall power loss of the system is used as the fitness, and the global optimal solution is approximated through selection, crossover, and mutation iteration. To reflect the differences in reliability and economy among different nodes, a comprehensive importance weight for each node is introduced. and node economic loss growth rate Weighting the power gap, the power loss expression for the system under the electric vehicle (EV) scheme is as follows (28): （28）； In equation (28): This refers to the total power loss of the system when only electric vehicles participate in power restoration; Assuming a comprehensive importance weight for each node, Increased rate of economic loss at nodes; and They are nodes at time 10:00 and 20:00 respectively. t Demand power and recovery power; S Total cost of switch operation; The total cost of using a collection of power banks within a single power cycle; Based on this, a comprehensive optimization objective function for the EV scenario only is constructed, as shown in equation (29): （29）； In equation (29): This is the comprehensive objective function value in the EV scenario only, and it is also the evaluation index of individual fitness in GA. The smaller the value, the better the solution. For the first j Power supply to load node a The shortest electrical distance; For nodes j The power supply capacity; This refers to the total power loss of the system when only electric vehicles participate in power restoration; , These are the weighting coefficients for distance cost and power outage loss, respectively; To ensure that the solution results satisfy the physical and operational constraints, the power allocation of EV also needs to be constrained during the GA iteration process. The typical constraint can be expressed as shown in equation (30): （30）； In equation (30): For the first j Vehicles towards the area k Power allocation ratio coefficient of power supply; The available power provided by the EV vehicle; For the region k The first constraint states that the sum of the vehicle power allocation ratio coefficients does not exceed 1, to ensure the vehicle power supply capacity constraint; the second constraint states that the node recovery power is neither negative nor does it exceed 1.1 times the demand power, to ensure system voltage stability.

10. The electric vehicle cooperative grid power supply restoration strategy considering multi-time-domain EV-EPSV as described in claim 9, characterized in that: In step 3, in order to overcome the capacity and space limitations of the EV-only solution, an emergency power supply vehicle is further introduced to participate in energy supply scheduling, forming a collaborative optimization strategy of EV static allocation and EPSV dynamic compensation. The first stage uses the genetic algorithm (GA) to complete the initial power and node allocation for the EVs; the second stage introduces an adaptive large neighborhood search algorithm to dynamically optimize the driving path and stopping order of the emergency power supply vehicles. Based on this, the EV group provides initial support, while EPSV provides mobile support to key nodes. Together, they form a regional collaborative power supply capability, and its micro power distribution form is shown in Equation (31): （31）； In equation (31): For the region k At any moment t The equivalent combined power supply; Power allocation factor; For nodes j The equivalent power; For emergency vehicles v To load k Power supplied; To simplify subsequent derivations, EV and EPSV are compared to the region. k The provided power is denoted as follows: and The equivalent representation of the combined power supply is obtained, as shown in equation (32): （32）； In equation (32): For the region k At any moment t Combined power supply of EV-EPSV; For the region k At any moment t Power supplied by the EV group; For the region k At any moment t Power supplied by EPSV; Since the importance and load scale of nodes vary in different regions, a regional weighting coefficient is introduced to reflect this unevenness in the system evaluation. The region weight is defined as shown in equation (33): （33）； In equation (33): For the region k The weighted importance coefficient comprehensively reflects the load scale and the weight of key nodes in the region; For the region k Set of internal nodes; For nodes i Importance weight; For nodes i The required power; To maintain symbol consistency, the region k The total power demand is denoted as Its definition is shown in equation (34): （34）； In equation (34): For the region k At any moment t The required power; For the region k At any moment t The load demand power; Considering the differences in load scale and critical node distribution across different regions, a regional weighting coefficient is introduced. Weighting the power deficit, the overall power loss of the system under the EV-EPSV collaborative recovery scheme is shown in equation (35): （35）； In equation (35): The overall power loss loss under the EV and EPSV collaborative power supply scheme; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply for EV and EPSV; S Total cost of switch operation; The total cost of using a collection of power banks within a single power cycle; When considering the balance between fairness and economy of power supply resources, a corresponding comprehensive optimization objective function is constructed. Then, the comprehensive optimization objective in the EV-EPSV collaborative scenario can be expressed as shown in equation (36): （36）； In equation (36): The comprehensive objective function value under the EV–EPSV collaborative power supply scheme is the fitness evaluation index in the ALNS search process; For the region k The weighted importance coefficient; For the region k At any moment t The required power; The combined power supply for EV and EPSV; λ is the weighting coefficient; The proportion of power supplied to the target node; The total nominal power supply capacity that can be provided for the EV-EPSV vehicle group during the cooperative power supply phase; the first item represents the residual load loss item; the second item represents the fairness penalty item; To ensure the optimization results are physically and operationally feasible, constraints on the EPSV output and service relationship are also introduced, as shown in equation (37): （37）； In equation (37): The combined power supply for EV and EPSV; For the region k At any moment t The required power; For emergency vehicles v To load k Power supplied; This represents the vehicle's maximum output capacity. A binary variable indicating whether the vehicle is enabled: 1 indicates enabled, 0 indicates disabled; Constraint variables are assigned to the power supply task. The first constraint states that the combined power supply power of the region cannot be negative and cannot exceed the power demand of the region, so as to ensure that the power supply level of each region is physically feasible and meets the basic supply and demand balance constraint. The second constraint states that the power provided by the emergency power supply vehicle to the region shall not exceed the maximum output capacity of the vehicle and shall be consistent with the start-stop state variable, so as to ensure that the output of a single vehicle does not exceed the limit and conforms to the vehicle's activation state. The third constraint states that the sum of the service allocation variables of each region at the same time does not exceed 1, so as to ensure that each region is served by at most one emergency power supply vehicle, thereby ensuring that the task allocation and route scheduling are feasible in actual operation. To quantify the economic differences between the two strategies across multiple scenarios and regions, a relative savings rate metric is introduced. As shown in equation (38): （38）； In equation (38): Indicates in the scene s area a The relative savings rate of the EV-EPSV collaborative power supply solution compared to the EV-only solution. The larger the value, the more obvious the economic advantage of the collaborative solution in this spatiotemporal scenario; Indicates in the scene s area a The overall power loss of the EV solution only; Indicates in the scene s area a Overall power loss of the EV-EPSV collaborative scheme; s Indicates the scene type; a Indicates the region category.

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