Optimized scheduling method for electric vehicle participating in power distribution network based on double-layer optimization model

Abstract

The invention discloses a double-layer optimization model-based optimization scheduling method for an electric vehicle to participate in a power distribution network, and the method comprises the steps: obtaining the geographic information, a pre-layout scheme and a fault scene of the power distribution network, and calculating a movable path, a moving distance and time of the electric vehicle in the power distribution network; establishing an economic excitation energy response model, and constructing a load recovery double-layer optimization model considering the benefit demand of the power distribution network and the electric vehicle based on a demand response mechanism and energy response; and according to the load recovery double-layer optimization model, inputting power distribution network operation parameters and resource configuration parameters, converting a double-layer optimization problem into a single-layer problem through a KKT condition and a large M method for solving, and outputting and obtaining a resource scheduling result, a fault recovery result and an electric vehicle income result. By optimizing the charging and discharging behaviors of the electric vehicle, the load recovery level of the power distribution network is improved, and the operation efficiency and reliability of the power grid are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization and dispatching, and in particular relates to an optimization and dispatching method for electric vehicles participating in a distribution network based on a double-layer optimization model. Background Art

[0002] With the widespread adoption of electric vehicles (EVs), their potential as distributed energy storage resources is gradually being explored. In distribution networks, EVs can participate in grid dispatch through vehicle-to-grid (V2G) technology, providing flexibility and improving grid efficiency and reliability. However, current methods for EVs to participate in distribution network dispatch have the following drawbacks:

[0003] Lack of effective incentive mechanism: Existing scheduling methods fail to fully consider the economic interests of electric vehicle users, resulting in low user participation and difficulty in achieving large-scale electric vehicle participation in scheduling.

[0004] The temporal and spatial distribution characteristics of electric vehicles are not taken into account: the temporal and spatial distribution of electric vehicles in the distribution network is uncertain. Existing methods are difficult to reasonably guide the distribution of electric vehicles, which affects the scheduling effect.

[0005] Lack of collaborative optimization: Distribution System Operators (DSOs) and electric vehicle users have different interests and goals. Existing methods fail to effectively coordinate the interests of both parties, making it difficult to achieve the optimal scheduling solution. Summary of the Invention

[0006] The present invention aims to provide an optimized scheduling method for electric vehicles participating in distribution networks based on a two-layer optimization model. By designing a reasonable demand response mechanism and optimization model, the defects of the existing technology are solved, the participation of electric vehicles is improved, the temporal and spatial distribution of electric vehicles is optimized, the interests of distribution network operators and electric vehicle users are coordinated, and the operating efficiency and reliability of the distribution network are improved.

[0007] To achieve the above objectives, the present invention provides an optimization scheduling method for electric vehicles participating in a distribution network based on a two-layer optimization model, comprising:

[0008] Obtain distribution network geographic information, pre-layout plans, and fault scenarios, and calculate the possible movement paths, distances, and time for electric vehicles in the distribution network;

[0009] Establishing an economic incentive energy response model, wherein the economic incentive energy response model includes an incentive-type response economic compensation model and a price-type response economic compensation model;

[0010] Based on the demand response mechanism and energy response, a two-level load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed;

[0011] According to the load recovery two-layer optimization model, the distribution network operating parameters and resource configuration parameters are input, and the two-layer optimization problem is converted into a single-layer problem through the KKT condition and the big M method for solution. The resource scheduling results, fault recovery results and electric vehicle revenue results are output.

[0012] Preferably, the process of establishing the economic incentive energy response model includes:

[0013] Before a disaster occurs, establish an incentive-based response economic compensation model;

[0014] According to the incentive-based response economic compensation model, different economic incentive compensation prices are set at different V2G stations to indirectly control the spatial distribution of electric vehicles that respond to recovery;

[0015] After a disaster causes a distribution network failure, a price-based response economic compensation model is established. The distribution network operator publishes the time-of-use charging and discharging prices during the scheduling period to electric vehicle users, and electric vehicles make their own energy response decisions in each time period based on the time-of-use charging and discharging prices.

[0016] Preferably, the incentive-type response economic compensation model uses a piecewise linear model to describe the relationship between the incentive-type response economic compensation price and the response degree of the electric vehicle;

[0017] The piecewise linear model includes a dead zone, a linear zone and a saturation zone.

[0018] Preferably, the process of constructing a load recovery two-layer optimization model based on demand response mechanism and energy response that considers the interests of the distribution network and electric vehicles includes:

[0019] A two-layer load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed. The upper layer is the distribution network operator decision model, and the lower layer is the electric vehicle decision model.

[0020] Preferably, the objective function of the distribution network operator decision model is to minimize the economic cost in restoration scheduling, including the cost of power loss load, the configuration and scheduling cost of flexibility resources, and the economic compensation cost for electric vehicles.

[0021] Preferably, the constraints of the distribution network operator decision model include flexibility resource scheduling constraints, distribution network operation constraints and price decision constraints in the economic incentive energy response model.

[0022] Preferably, the objective function of the electric vehicle decision model is to maximize the economic benefits in the recovery scheduling, including incentive-type response economic compensation benefits, price-type response economic compensation benefits and residual power satisfaction equivalent benefits.

[0023] Preferably, the constraints of the electric vehicle decision model include power and output constraints of the V2G station, and charging and discharging behavior constraints of the electric vehicle.

[0024] Preferably, the process of converting the two-level optimization problem into a single-level problem by using the KKT condition and the big M method for solving the problem includes:

[0025] The electric vehicle decision model at the lower level in the load recovery two-level optimization model is transformed into additional constraints of the distribution network operator decision model at the upper level, and the two-level optimization problem is transformed into a single-level problem and solved.

[0026] Preferably, the process of converting the lower-layer electric vehicle decision model in the load restoration two-layer optimization model into additional constraints of the upper-layer distribution network operator decision model includes:

[0027] Based on the KKT condition, the lower-level electric vehicle decision model is transformed into additional constraints for the upper-level distribution network operator decision model;

[0028] The complementary relaxation conditions obtained after transforming the lower-level electric vehicle decision model are linearized using the big M method.

[0029] Compared with the prior art, the present invention has the following advantages and technical effects:

[0030] Improve electric vehicle participation: By designing a reasonable economic incentive mechanism, the economic interests of electric vehicle users are fully considered, thereby improving user participation.

[0031] Optimize the spatiotemporal distribution of electric vehicles: Through an incentive-based response economic compensation model, the spatial distribution of electric vehicles is rationally guided, improving the scheduling effect.

[0032] Coordinate interest goals: Through a two-layer optimization model, the interests of distribution network operators and electric vehicle users are coordinated to achieve optimal scheduling for both parties.

[0033] Improve the operating efficiency of the distribution network: By optimizing the charging and discharging behavior of electric vehicles, the load recovery level of the distribution network is improved, and the operating efficiency and reliability of the power grid are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0035] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0036] Figure 2 Schematic diagram of a linear model of incentive-type response economic compensation price and EV response degree according to an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of active power levels for power restoration in different time periods based on the total number of EVs in different areas according to an embodiment of the present invention;

[0038] Figure 4 Schematic diagram of charging and discharging prices and EV charging and discharging power under different remaining power satisfaction coefficients according to an embodiment of the present invention;

[0039] Figure 5 This is a first schematic diagram of active power output and time-space movement of the MESS under different remaining power satisfaction coefficients according to an embodiment of the present invention;

[0040] Figure 6 This is a second schematic diagram of the active power output and time-space movement of the MESS under different remaining power satisfaction coefficients according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Example 1

[0044] like Figure 1-6 As shown, this embodiment provides an optimization scheduling method for electric vehicles participating in a distribution network based on a two-layer optimization model, including:

[0045] Obtain distribution network geographic information, pre-layout plans, and fault scenarios, and calculate the possible movement paths, distances, and time for electric vehicles in the distribution network;

[0046] Establish an economic incentive energy response model, which includes an incentive-type response economic compensation model and a price-type response economic compensation model;

[0047] Based on the demand response mechanism and energy response, a two-level load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed;

[0048] According to the load recovery two-layer optimization model, the distribution network operating parameters and resource configuration parameters are input, and the two-layer optimization problem is converted into a single-layer problem through the KKT condition and the big M method for solution. The output is the resource scheduling results, fault recovery results and electric vehicle revenue results.

[0049] Furthermore, the process of establishing an economic incentive energy response model includes:

[0050] Before a disaster occurs, establish an incentive-based response economic compensation model;

[0051] According to the incentive-based response economic compensation model, different economic incentive compensation prices are set at different V2G stations to indirectly control the spatial distribution of electric vehicles that respond to recovery;

[0052] After a disaster causes a distribution network failure, a price-based response economic compensation model is established. The distribution network operator publishes the time-of-use charging and discharging prices during the scheduling period to electric vehicle users, and electric vehicles make their own energy response decisions in each time period based on the time-of-use charging and discharging prices.

[0053] Specifically, power demand response refers to the use of economic incentives by distribution system operators (DSOs) to encourage power users to adjust their flexible loads, thereby changing their electricity consumption behavior, reducing power consumption or adjusting the time of use, achieving the goal of peak load shifting and valley filling, and ensuring the safe and stable operation of the power system. This embodiment draws on the demand response model to provide certain emergency economic incentives to flexible loads in the distribution network, such as electric vehicles (EVs), so that they respond with corresponding energy in the short term and participate in load power restoration.

[0054] Based on the driving method, demand response can be divided into two types: incentive-based and price-based. Incentive-based demand response (IBDR) refers to the direct use of incentive policies and compensation methods, signing contracts with electricity users, setting incentive prices, and users receiving benefits based on the incentive and compensation mechanism agreed in the contract. Price-based demand response (PBDR) refers to the use of time-of-use electricity prices and other methods to guide users to adjust their electricity consumption behavior through price changes. With reference to these two models, this embodiment designs two economic incentive energy response models for EVs. Before a disaster occurs, incentive-based response economic compensation methods are used to guide EVs to the DSO's desired V2G stations to prepare for post-disaster power supply. After a disaster occurs, price-based response economic compensation methods are used to guide EVs to charge and discharge at different times to simultaneously meet the DSO's load recovery needs and EV economic benefits.

[0055] Furthermore, the incentive-type response economic compensation model uses a piecewise linear model to describe the relationship between the incentive-type response economic compensation price and the responsiveness of electric vehicles;

[0056] The piecewise linear model includes a dead zone, a linear region, and a saturation region.

[0057] Specifically, before a disaster is about to occur, an incentive policy is issued through the DSO to provide financial compensation to EV users who arrive at the V2G station to respond to the load recovery scheduling plan, and an agreement is signed with the EV users. EV users will fully charge their vehicles in preparation for post-disaster power supply, and they cannot disconnect the EV from the distribution network after the disaster causes a distribution network failure until the fault recovery scheduling is completed, otherwise it will be considered a breach of contract.

[0058] The energy distribution of energy storage resources is closely linked to the level of recovery. However, the spatial distribution of EV energy during load recovery is limited by the location of the V2G station, and EVs cannot move during the recovery scheduling process. Furthermore, given the large number of EVs and the uncertainty of their spatial and temporal distribution within the distribution network, it is difficult for the DSO to develop reasonable routing guidance for each EV and ensure EV user compliance. Therefore, this embodiment proposes an incentive-based response economic compensation model. By setting different economic incentive compensation prices at different V2G stations, it indirectly controls the spatial distribution of EVs responding to recovery to meet the DSO's expectations.

[0059] Referring to the consumer psychology model, a piecewise linear model is used to describe the relationship between the incentive response economic compensation price and the EV response degree. Figure 2 As shown in the figure, the incentive-based response economic compensation price has three ranges: the dead zone, the linear zone, and the saturation zone. When the incentive price offered by the DSO is below the dead zone threshold (the minimum incentive price), EVs will not respond to the DSO's load restoration plan. When the incentive price exceeds the minimum incentive price, the EV's response increases linearly with the increase in the incentive price. When the EV's response reaches the maximum response level, increasing the incentive price will no longer increase its response level, indicating that it has entered the saturation zone.

[0060] Furthermore, after a disaster causes a distribution network failure, the DSO begins dispatching various flexible resources for fault recovery and load restoration. With the involvement of these various resources, the distribution network's load supply status and network topology are constantly changing, and the energy demands of the EVs participating in the recovery process at the V2G station also fluctuate. In other words, in addition to guiding the spatial distribution of EVs, the DSO should also regulate the temporal distribution of EV energy. Although the DSO and EV users have signed agreements and provided certain incentive-based economic compensation to EV users, they still have different stakeholders, and EVs cannot fully accept the DSO's real-time scheduling as they do with the MESS. Therefore, a different economic compensation mechanism, based on the DSO's temporal energy demands and the interests of EV users, is needed to guide EVs in making different charging and discharging decisions at different stages of the fault recovery scheduling cycle.

[0061] Currently, time-of-use electricity pricing strategies are being implemented across my country. Electricity users experience peak and valley periods in their daily load demand. By setting different electricity prices for different time periods, users' electricity consumption curves can be indirectly regulated, achieving a certain degree of peak-shaving and valley-filling. This time-of-use pricing mechanism can also be applied to DSOs' guidance of EV charging and discharging behavior. Drawing on the time-of-use pricing mechanism in demand response, this embodiment proposes a price-based response economic compensation model for EVs. Before the start of fault recovery scheduling, the DSO publishes the time-of-use charging and discharging prices for the scheduling period to EV users. EVs then make their own energy response decisions for each time period based on these prices, thereby simultaneously satisfying the DSO's need to improve load recovery and the EV's need to generate economic benefits.

[0062] Furthermore, the process of constructing a two-level load recovery optimization model based on demand response mechanism and energy response, which considers the interests of distribution network and electric vehicles, includes:

[0063] A two-layer load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed. The upper layer is the distribution network operator decision model, and the lower layer is the electric vehicle decision model.

[0064] Specifically, due to the different interests of DSOs and EVs in load recovery scheduling, this embodiment constructs the EV participation in distribution network load recovery scheduling strategy into a two-layer optimization model based on two economic incentive energy response models. The upper layer is the DSO decision-making model, and the objective function is to minimize the economic cost in recovery scheduling. It also provides the economic incentive price for arriving at the V2G station for response and recovery before the disaster and the time-sharing charging and discharging price signal after the disaster; the lower layer is the EV decision-making model, and the objective function is to maximize the economic benefits in recovery scheduling. EVs feed back their own energy response strategies to the upper layer model based on the price signals provided by the DSO until the model reaches equilibrium and the optimal strategy is obtained.

[0065] Furthermore, the objective function of the distribution network operator decision model is to minimize the economic cost in restoration scheduling, including the cost of power loss load, the configuration and scheduling cost of flexibility resources, and the economic compensation cost for electric vehicles.

[0066] Specifically, DSOs should consider both effectiveness and economy in restoration scheduling. They need to include the cost of power outage loads and the configuration and scheduling costs of flexibility resources in the objective function. When EVs participate in restoration scheduling, the economic compensation costs for EVs must also be considered. Therefore, the objective function of the upper-level model is to minimize the total economic cost of the DSO:

[0067] min C DSO =C DSII +C IBR +C PBR (1)

[0068] C DSII =C PLS +C OP +C FL +C RC (2)

[0069]

[0070]

[0071] Where: C DSO is the total economic cost of DSO. DSII is the sum of the power loss load cost and various resource allocation costs in fault recovery scheduling; C PLS is the power loss load cost; C OP is the circuit breaker operation cost; C FL is the line failure maintenance cost; C RC is the RC maintenance cost. IBR is the economic compensation cost for incentive response, i.e., the economic compensation cost for EVs arriving at the V2G station in response to power supply restoration; c IBR,v is the economic incentive price that the vth V2G station responds to for each EV; N E,v is the number of EVs arriving at the vth V2G station. PBR is the economic compensation cost of price-type response, that is, the economic compensation cost of charging and discharging EV in each period; c PBR,t is the charging and discharging price in period t; P Vd,v,t and P Vc,v,t is the discharge power and charging power of the vth V2G station in the tth period; V is the set of V2G stations; T is the total duration of fault recovery scheduling.

[0072] Furthermore, the constraints of the distribution network operator decision model include flexibility resource scheduling constraints, distribution network operation constraints, and price decision constraints in the economic incentive energy response model.

[0073] Specifically, the upper-level model constraints include flexibility resource scheduling constraints, distribution network operation constraints, and price decision constraints in the economic incentive energy response model. The following constraints are used to express the linear relationship between the incentive response economic compensation price and the EV response degree:

[0074]

[0075]

[0076] Where: r IBR,v is the EV response degree of the vth V2G station; r IBR,max is the maximum response level; N E,v is the number of EVs that respond to the vth V2G station and participate in the recovery; N E,max is the total number of EVs in the region; c IBR,v k is the incentive economic compensation price for each EV that responds to and participates in the restoration of the vth V2G station; IBR,v is the EV response coefficient of the vth V2G station. The smaller the value, the greater the EV response degree under the same incentive price; c IBR,0 is the minimum incentive price for EV to respond.

[0077] In actual situations, the EV response degree is uncertain, and the uncertainty of the response coefficient is described by chance constraint:

[0078]

[0079] Where: Pr(·) represents the probability of an event, ξ is the confidence level, and the constraint characterizes that the EV response coefficient is not less than the estimated value k IBR,est The probability of is not less than ξ. Convert this chance constraint into a deterministic constraint:

[0080]

[0081] Where: γ v A 0-1 variable that represents whether the response coefficient of the vth V2G station is not lower than the estimated value. If it is 1, it means it is not lower than the estimated value, otherwise it is 0; N V is the total number of V2G stations; k IBR,lim is the limit value of the EV response coefficient.

[0082] In addition, the economic incentive price c of the incentive-type response economic compensation IBR,v Time-of-use charging and discharging price c of price-type response economic compensation cost PBR,t Should be within a certain range:

[0083]

[0084]

[0085] Where: c IBR,max 、c IBR,min and c PBR,max 、c PBR,min c IBR,v and c PBR,t The upper and lower limits of two prices.

[0086] The total number of EVs participating in the response and the number of EVs that can be accommodated at each V2G station should also be limited:

[0087]

[0088]

[0089] Where: N E,max,v is the number of charging and discharging piles at the vth V2G station.

[0090] In addition to the decision constraints on the two economic compensations for EVs, the upper-level decision model also needs to satisfy the MESS scheduling constraints, SVC scheduling constraints, RC scheduling constraints, network reconstruction constraints, and the operation requirements of the distribution network.

[0091] Furthermore, the objective function of the electric vehicle decision-making model is to maximize the economic benefits in the recovery dispatch, including the incentive-based response economic compensation benefits, the price-based response economic compensation benefits and the equivalent benefits of the remaining power satisfaction.

[0092] Specifically, in addition to considering the two economic compensation benefits provided by the DSO, EV users also need to consider their own travel needs after the restoration scheduling is completed, that is, the demand for remaining electricity. Therefore, the objective function of the lower-level model is to maximize the total benefit of the EV user group:

[0093] max C EV =C IBR +C PBR +C SOC (13)

[0094]

[0095] Where: C EV is the total benefit of the EV group; C IBR and C PBR are the incentive-based response economic compensation benefits and the price-based response economic compensation benefits respectively; C SOC E is the equivalent benefit of the EV group’s remaining power satisfaction, V,v,Tis the total remaining power of EVs at the vth V2G station at the end of the fault recovery scheduling period, c SOC is the remaining power satisfaction coefficient.

[0096] Furthermore, the constraints of the electric vehicle decision model include the power and output constraints of the V2G station, as well as the charging and discharging behavior constraints of the electric vehicle.

[0097] The constraints of the lower-level decision model need to meet the power and output constraints of the V2G station.

[0098] The charging and discharging power of a V2G station is limited by the number of EVs arriving at the station:

[0099]

[0100] Where: P Ecmax and P Edmax It is the maximum charging and discharging power of an EV.

[0101] The power consumption of V2G stations is also limited by the number of EVs and is related to the charging and discharging power:

[0102]

[0103]

[0104]

[0105] Where: E Emax and E Emin E is the upper and lower limits of an EV's power; V,v,t is the power consumption of the vth V2G station; E V,v,1 is the total remaining power of EVs at the vth V2G station during the start period of fault recovery scheduling; η Vc,v and η Vd,v is the charge and discharge efficiency.

[0106] Considering the travel needs of users after leaving the grid, the power of EVs in the V2G station needs to be restored to at least a certain value before the fault recovery scheduling ends:

[0107]

[0108] Where: r E is the percentage of remaining power, r E,min The lowest percentage of remaining power.

[0109] In addition, like MESS, the charging and discharging of EVs in V2G stations cannot occur simultaneously and must meet the following requirements:

[0110]

[0111] When the product of charge and discharge efficiency η Vc,v ·η Vd,v When <1 is strictly established, the above formula can be relaxed. Since the charge and discharge efficiency must satisfy 0<η in actual situations Vc,v <1 and 0 <η Vd,v <1, so the inequality is strictly true and the above formula can be relaxed.

[0112] Furthermore, the process of converting the two-level optimization problem into a single-level problem by using the KKT condition and the Big M method for solution includes:

[0113] The electric vehicle decision model at the lower level in the load recovery two-level optimization model is transformed into additional constraints of the distribution network operator decision model at the upper level, and the two-level optimization problem is transformed into a single-level problem and solved.

[0114] Furthermore, the process of converting the lower-level electric vehicle decision model in the load restoration two-level optimization model into the additional constraints of the upper-level distribution network operator decision model includes:

[0115] Based on the KKT condition, the lower-level electric vehicle decision model is transformed into additional constraints for the upper-level distribution network operator decision model;

[0116] The complementary relaxation conditions obtained after transforming the lower-level electric vehicle decision model are linearized using the big M method.

[0117] Specifically, based on the KKT conditions, the lower-level model can be transformed into additional constraints for the upper-level decision model, transforming the two-level optimization problem into a single-level problem. The complementary relaxation conditions obtained after the transformation of the lower-level model are then linearized using the Big M method. The transformed single-level optimization model can then be solved using the YALMIP toolbox and the GUROBI solver in the MATLAB environment.

[0118] More specifically, based on the transformation of the lower-level model under the KKT condition, the Lagrangian function of the lower-level optimization problem is as follows:

[0119]

[0120] Where: and are the dual variables of the four inequality constraints in formula (15); and are the dual variables of the two inequality constraints in formula (16); is the dual variable of the inequality constraint (19); is the dual variable of the equality constraint (17); is the dual variable of the equality constraint (18).

[0121] For variable P Vc,v,t 、P Vd,v,t and E V,v,t and the equality constraint dual variable and Find the partial derivative:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] The complementary relaxation conditions after the transformation of the inequality constraints of the lower model are as follows:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Where: 0≤x⊥y≥0 means that at most one of the variables x and y can be strictly greater than 0, that is, x≥0 and y≥0 and x·y=0.

[0136] Furthermore, in order to solve the model using existing commercial solvers, the complementary slack conditions need to be linearized. This implementation uses the Big M method to convert the complementary slack conditions into linear constraints:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] Where: and is a 0-1 variable introduced to linearize the complementary relaxation conditions (27) to (33).

[0152] Example 2

[0153] This embodiment is also tested based on the improved IEEE 33 distribution network. The total system load is 3.715+j2.3MVA, the rated voltage is 12.66kV, the allowable voltage deviation is ±7% of the rated voltage, and the maximum apparent transmission capacity of the line is 6.5MVA.

[0154] Assume that the fault points are all midpoints of the line, the fault scenario is the worst fault scenario, the actual travel distance of all mobile resources is 1.5 times the straight-line distance between the starting and ending points, and the moving speed is 45 km·h-1.

[0155] EVs that signed agreements before a disaster occurred were fully charged at various V2G stations to prepare for post-disaster power supply. They remained connected to the grid during the recovery period. The parameters for the economic compensation model, as well as those for the EVs and V2G stations, are shown in Tables 1-3. A two-level optimization model that takes into account the energy response of EV economic incentives was solved. The recovery period lasted one hour, and the simulation step size was set to one hour.

[0156] Table 1

[0157]

[0158]

[0159] Table 2

[0160]

[0161] Table 3

[0162]

[0163] The economic compensation model parameter settings are shown in Table 1, the EV parameter settings are shown in Table 2, and the V2G station parameter settings are shown in Table 3. E,max The impact on the distribution network restoration results and EV revenue results shows the superiority of the proposed strategy. Figure 3 The economic cost and load recovery results of DSO are shown in Table 4, and the EV benefit results are shown in Table 5.

[0164] First, we analyze from the perspective of DSO interests. Figure 3 As can be seen from the power supply restoration level, as the total number of EVs in the region increases, the load active power restoration level in the early stages of fault recovery increases, and the restoration level remains essentially unchanged after the total number of EVs exceeds 100. Table 4 shows the economic cost and load restoration results for DSO. When the total number of EVs in the region is zero, i.e., when no EVs participate in the restoration strategy, the Level 2 load restoration rate is only 86.95%, resulting in the highest power loss load cost. However, when the total number of EVs in the region is 100, the proposed strategy uses EVs in V2G stations to supply power to the power loss load area in the early stages of fault recovery, achieving a Level 2 load restoration rate of 100%, significantly reducing the power loss load cost. Therefore, even after accounting for the economic compensation costs for EVs, the total cost of DSO is significantly lower than the comparison strategy without EVs participating in the restoration.

[0165] When there are only 50 EVs in the region, power supply to the Level 2 load cannot be fully restored in the early stages of fault recovery due to the limitations of the total number of EVs and the upper limit of the economic incentive price. Therefore, the cost of the power-lost load is higher than when the total number of EVs is 100, and the economic incentive price for each EV is also increased. However, the total cost of the DSO is still much lower than the comparison strategy without EV participation in the restoration. This shows that the strategy of economically incentivizing EVs to respond to power restoration can effectively and economically improve the resilience of the distribution network at different EV penetration rates.

[0166] When the total number of EVs increases to 200, the number of EV responses remains unchanged compared to the case with only 100 EVs. This is because the participation of 58 EVs is sufficient to fully restore the power supply of the Level 2 load, while the unit power loss of Level 1 and Level 2 loads is as high as 200 yuan / kW. -1 and 20 yuan·kW -1 , level 3 load is 1 yuan·kW -1, considering the economic feasibility, no longer increasing the economic compensation cost to attract more EVs to restore power supply to level 3 loads.

[0167] Let's analyze this from the perspective of EV benefits. The EV benefit results in Table 5 show that when the total number of EVs in a region is relatively small, at only 50, the DSO will increase the economic incentive price to increase the EV response rate in the region, thereby attracting as many EVs as possible to participate in power restoration, and the average EV benefit will also be higher. As the EV penetration rate in the region increases, after meeting the requirement for full restoration of critical loads, the DSO can use lower economic incentive prices to achieve the same expected number of EV responses. Therefore, the average benefit of EV incentive-based response economic compensation will decrease. However, since the charge and discharge volume remains unchanged, the same price-based response economic compensation and residual power satisfaction equivalent benefits can still be obtained, and overall each EV can also obtain a reasonable average benefit.

[0168] Table 4

[0169]

[0170] Table 5

[0171]

[0172] This embodiment analyzes the remaining power satisfaction coefficient c SOC Impact on distribution network load restoration and EV revenue outcomes. Figure 4 This is the time-of-use charging and discharging price set by the DSO under different remaining power satisfaction coefficients and the charging and discharging power diagram of EV in each period. Figure 5 and Figure 6 c SOC =0.5 and c SOC = 0.8, the active power output and spatiotemporal movement of MESS, Table 6 shows the DSO economic cost, Table 7 shows the EV benefit results, and Table 8 shows the specific price response results and the percentage of remaining power of EV.

[0173] The increase in the remaining power satisfaction coefficient means that EV users are more inclined to retain more remaining power for travel, rather than earning more price-based response economic benefits through the difference between charging and discharging. At this time, DSO needs to increase the overall level of charging and discharging prices to encourage EV to discharge. Figure 4The EV charge and discharge power at each time period and the total EV charge and discharge volume in Table 8 show that, compared to the case with a satisfaction coefficient of 0.5, the EV discharge volume slightly decreases when the satisfaction coefficient is 0.8, while the charge volume significantly increases. Furthermore, the DSO-set charge and discharge prices are generally higher than those for the case with a satisfaction coefficient of 0.5. Furthermore, in both cases, the charge and discharge prices in the early stages of fault recovery are higher than those in the later stages. This is because the DSO encourages EVs to discharge in the early stages of a more severe power outage to earn financial benefits, and to charge in the later stages of a faulty line, when repairs are largely complete, to meet travel needs.

[0174] In addition, since the EV was charged with high power in the late stage of fault recovery when the satisfaction coefficient was 0.8, Figure 5 It can be seen that in this case, the MESS is charged in periods 6-10 and moves to the terminal node to output active power in the later stage of recovery. This is also to reduce the line voltage drop and raise the terminal voltage level to ensure that the node voltage level of the distribution network remains within a safe range when the EV is charged significantly.

[0175] From the perspective of DSO interests, Table 6 shows that when the satisfaction coefficient is 0.8, the EV discharge volume is slightly reduced, so the distribution network power loss load cost increases slightly, but the EV charging volume increases significantly, so the DSO's economic compensation to EVs also decreases accordingly, and the overall DSO's total cost decreases.

[0176] From the perspective of EV benefits, Table 8 shows that when the satisfaction coefficient is 0.8, the remaining power percentage of the EV group reaches 100%. Therefore, Table 7 shows that the equivalent benefit of remaining power satisfaction has increased significantly, while the higher charging cost makes the price-based response economic benefit 0. The average benefit has increased compared to the case with a satisfaction coefficient of 0.5.

[0177] Overall, there is no significant difference in the total cost of the DSO and the average revenue of EVs between the two scenarios. This indicates that the proposed strategy allows the DSO to guide the charging and discharging behavior of EVs by setting different levels of time-of-use charging and discharging prices, thereby reasonably meeting its own load recovery needs and EV revenue needs under different remaining power satisfaction coefficients.

[0178] Table 6

[0179]

[0180] Table 7

[0181]

[0182] Table 8

[0183]

[0184] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing the dispatch of electric vehicles participating in a distribution network based on a two-layer optimization model, characterized in that: include: Obtain distribution network geographic information, pre-layout plans, and fault scenarios, and calculate the possible movement paths, distances, and time for electric vehicles in the distribution network; Establishing an economic incentive energy response model, wherein the economic incentive energy response model includes an incentive-type response economic compensation model and a price-type response economic compensation model; Based on the demand response mechanism and energy response, a two-level load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed; According to the load recovery two-layer optimization model, the distribution network operating parameters and resource configuration parameters are input, and the two-layer optimization problem is converted into a single-layer problem through the KKT condition and the big M method for solution. The resource scheduling results, fault recovery results and electric vehicle revenue results are output.

2. The method according to claim 1, characterized in that The process of establishing the economic incentive energy response model includes: Before a disaster occurs, establish an incentive-based response economic compensation model; According to the incentive-based response economic compensation model, different economic incentive compensation prices are set at different V2G stations to indirectly control the spatial distribution of electric vehicles that respond to recovery; After a disaster causes a distribution network failure, a price-based response economic compensation model is established. The distribution network operator publishes the time-of-use charging and discharging prices during the scheduling period to electric vehicle users, and electric vehicles make their own energy response decisions in each time period based on the time-of-use charging and discharging prices.

3. The method according to claim 1, characterized in that The incentive-type response economic compensation model uses a piecewise linear model to describe the relationship between the incentive-type response economic compensation price and the response degree of electric vehicles; The piecewise linear model includes a dead zone, a linear zone and a saturation zone.

4. The method according to claim 1, wherein The process of constructing a two-level load restoration optimization model based on demand response mechanism and energy response, which considers the interests of distribution network and electric vehicles, includes: A two-layer load recovery optimization model considering the interests of the distribution network and electric vehicles is constructed. The upper layer is the distribution network operator decision model, and the lower layer is the electric vehicle decision model.

5. The method according to claim 4, characterized in that The objective function of the distribution network operator decision model is to minimize the economic cost in restoration scheduling, including the cost of power loss load, the configuration and scheduling cost of flexibility resources, and the economic compensation cost for electric vehicles.

6. The method according to claim 4, characterized in that The constraints of the distribution network operator decision model include flexibility resource scheduling constraints, distribution network operation constraints, and price decision constraints in the economic incentive energy response model.

7. The method according to claim 4, characterized in that The objective function of the electric vehicle decision-making model is to maximize the economic benefits in the recovery scheduling, including the incentive-type response economic compensation benefits, the price-type response economic compensation benefits and the equivalent benefits of the remaining power satisfaction.

8. The method according to claim 4, characterized in that The constraints of the electric vehicle decision model include the power and output constraints of the V2G station and the charging and discharging behavior constraints of the electric vehicle.

9. The method according to claim 1, characterized in that The process of converting a two-level optimization problem into a single-level problem by using KKT conditions and the Big M method for solution includes: The electric vehicle decision model at the lower level in the load recovery two-level optimization model is transformed into additional constraints of the distribution network operator decision model at the upper level, and the two-level optimization problem is transformed into a single-level problem and solved.

10. The method according to claim 9, characterized in that The process of converting the lower-level electric vehicle decision model in the load restoration two-level optimization model into additional constraints for the upper-level distribution network operator decision model includes: Based on the KKT condition, the lower-level electric vehicle decision model is transformed into additional constraints for the upper-level distribution network operator decision model; The complementary relaxation conditions obtained after transforming the lower-level electric vehicle decision model are linearized using the big M method.

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