Regional power supply strategy optimization method under power supply interruption of power distribution network

Abstract

The invention belongs to the technical field of power distribution network dispatching optimization methods, and particularly relates to a regional power supply strategy optimization method under power supply interruption of a power distribution network. Comprising the following steps: establishing a post-outage power supply strategy model in a regional power supply period T; establishing an objective function of the optimal power supply effect in the regional power supply period; performing strategy optimization based on the optimal benefit of distributed power supply discharge; the regional power supply strategy optimization method under power supply interruption of the power distribution network is mainly used for optimizing a power supply mode of a distributed power supply, an energy storage station and a new energy charging and discharging station for the regional power distribution network under the condition of considering abnormal regional power supply, so as to ensure that temporary electric quantity supplementation is obtained as far as possible in the region to meet the power demand of a key occasion, and improve the power supply efficiency of the power distribution network. Therefore, emergencies can be dealt with.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network dispatch optimization methods, and in particular relates to a method for optimizing regional power supply strategies under distribution network outages. Background Art

[0002] Affected by various unexpected situations and uncontrollable events, it is still inevitable to temporarily cut off the supply to some areas in the current power supply network environment to meet specific needs, including power supply to important targets, fault isolation, etc. In these supply cut-off areas, there are still some important targets that are greatly affected by the supply cut-off, including hospitals, important infrastructure, important activities, etc. During the temporary supply cut-off period, the distribution network needs to timely dispatch various distributed power sources and other temporary power supply measures to meet the power demand in the above scenarios. With the rapid promotion of new energy vehicles, considering the charging and discharging capabilities of new energy vehicles and the characteristics of short use time and long stay time of most new energy electric vehicles, using new energy electric vehicles to provide assistance for temporary charging and discharging scheduling has become a feasible solution. By setting up charging and discharging stations in important infrastructure or activity areas such as hospitals, while facilitating the use of multiple new energy electric vehicles, it can be considered to use them as a relatively stable energy storage charging and discharging platform to meet the power supply needs in the above specific situations, thereby reducing the pressure on the distribution network scheduling and improving the stability of regional power supply. Summary of the invention

[0003] The purpose of the present invention is to provide a method for optimizing regional power supply strategies under distribution network outages, based on actual needs, by optimizing different power supply and distribution strategies within the power supply cycle to increase the discharge of new energy electric vehicles during the power outage period, increase the participation of new energy electric vehicle owners in peak shaving and valley filling, reduce the operating load of the distribution network, and improve the continuous operation stability of the distribution network.

[0004] To achieve the above purpose, the present invention adopts the following technical solution.

[0005] A method for optimizing regional power supply strategy under power outage of distribution network comprises the following steps:

[0006] Step 1: Establish a power supply strategy model after power outage within the regional power supply cycle T;

[0007] Establish the objective function of the best power supply effect within the regional power supply cycle T

[0008]

[0009] Where Q(t) = Q1(t) + Q2(t) + Q3(t) refers to the regional power supply capacity;

[0010] Q1(t) refers to the discharge capacity of distributed power sources at time t; Q2(t) refers to the discharge capacity of energy storage sites at time t; Q3(t) refers to the discharge capacity of new energy electric vehicles at time t; L(t) is the load value of key regional equipment at time t;

[0011] The DG discharge capacity Q1(t) is limited by the DG output power constraint, that is, 0≤Q1(t)≤Q 1.max ;

[0012] The energy storage site discharge capacity Q2(t) is subject to site operating conditions, including:

[0013] The power constraint is

[0014] Energy conservation constraint, that is

[0015] The charge and discharge power limit constraint is

[0016] Where S0 represents the initial charge, S t,min Indicates the lower limit of the energy storage charge state at time t, S t,max Indicates the upper limit of the energy storage charge state at time t; E ch Refers to the actual storage capacity of the energy storage site; P ch (t) represents the charging rate, P dis represents the discharge rate, t2 represents the charging period of the energy storage site; t1 represents the discharging period of the energy storage site; P ch.max (t) represents the maximum charge value; P dis.max (t) represents the maximum discharge value; Q 1.max Indicates the maximum discharge capacity of distributed power supply; K ch =1 or 0 indicates the charging mark value; K dis =1 or 0 indicates the discharge standard quality; K ch +K dis =1;

[0017] The discharge capacity Q3(t) of new energy electric vehicles is constrained by the charging and discharging state of electric vehicles, that is,

[0018] Where L v (t) is the charging status of new energy electric vehicles, P i ev (t) indicates that the i-th new energy electric vehicle is in the charging state; P i evd (t) indicates that the i-th new energy electric vehicle is in the discharging state;

[0019] Based on the above objective function and constraint conditions, the optimization goal is established to determine the power supply range that can meet the stable power supply within the normal power supply cycle T of the region. The power supply range conditions are then used to discharge distributed power sources, discharge new energy electric vehicles and power storage sites to meet the power load demand of key equipment in the region. On this basis, the output capacity of distributed power sources such as photovoltaics should be increased as much as possible to reduce the output dependence on new energy vehicles and energy storage sites as much as possible, make full use of the current power generation power, and retain energy storage redundancy as much as possible.

[0020] As the output party of distributed power sources, in order to improve their output willingness, the power supply strategy should be further optimized with the goal of optimizing the discharge benefit of distributed power sources, that is,

[0021] Step 2: Strategy optimization based on the best benefit of the distributed power supply discharge;

[0022] The objective function model of the optimal benefit of distributed generation discharge can be expressed as:

[0023]

[0024] Where R represents the discharge benefit of distributed power supply; C sale represents the income from the sale of electricity from distributed power sources to new energy electric vehicles; C ba is the peak-to-valley arbitrage income; C buy The cost of purchasing electricity to maintain the operation of the system; C op is the equipment operating cost; L v (t) is the charging load demand of new energy electric vehicles at time t; ρ c (t) is the unit price of distributed power supply at time t; ρ gr (t) represents the time-of-use electricity price of the distribution network at time t; P gr (t) represents the amount of electricity purchased from the distribution network at time t; P pv (t) represents the actual power supply to the region at time t; ξ pv (t) represents the generation cost of distributed generation at time t; ξ ba (t) represents the distributed generation energy storage cost at time t.

[0025] Further improvement or optimal implementation steps of the above-mentioned regional power supply strategy optimization method under distribution network outage, for the two main factors affecting the discharge willingness of new energy electric vehicle owners, road traffic status and new energy electric vehicle SOC level, their fuzzy classifications are established respectively, including four fuzzy subsets {poor, general, better, good} for representing the road traffic status x1 and three fuzzy subsets {insufficient, medium, sufficient} for representing the new energy electric vehicle SOC level x2;

[0026] Among them, the poor element in the road traffic status fuzzy subset indicates that the road communication status is very poor and congested, the average element indicates that the road traffic status is interfered and prone to congestion, the better element indicates that the road traffic status is good and not prone to congestion, and the good element indicates that the road traffic status is very good and not congested; the insufficient element in the new energy electric vehicle SOC level fuzzy subset indicates that the battery capacity of the new energy electric vehicle cannot meet the power demand of the next travel plan, the medium element indicates that the battery capacity of the new energy electric vehicle basically meets the power demand of the next travel plan, and the sufficient element indicates that the battery capacity of the new energy electric vehicle fully meets the power demand of the next travel plan;

[0027] The above fuzzy rules are expressed as:

[0028]

[0029] in represents the fuzzy subset corresponding to the road traffic state x1 input under the uth fuzzy rule; represents the fuzzy subset corresponding to the SOC level x2 input of the new energy electric vehicle under the uth fuzzy rule; f u (·) represents the linear function value corresponding to the u-th fuzzy rule; λ u , represents the fixed parameter of the uth fuzzy rule, u=1,2....U represents the sequence number of the fuzzy rule, and U is the total number of fuzzy rules;

[0030] Based on the TSK fuzzy rule model, the discharge willingness of new energy electric vehicles can be expressed as

[0031] in represents the membership function of the fuzzy subsets of road traffic status and the fuzzy subsets of SOC level of new energy electric vehicles;

[0032] Assuming the number of electric vehicles in the region is N3, the expected discharge capacity of new energy electric vehicles at time t can be expressed as Q3(t) = θ·P 3.dis (t)·N3; where P 3.dis (t) represents the discharge power of a single new energy electric vehicle.

[0033] Further improvement or optimal implementation steps of the above-mentioned regional power supply strategy optimization method under distribution network outage also include

[0034] Through the discharge price incentive measures, the willingness of new energy electric vehicle owners to discharge can be further improved, and a larger discharge capacity of electric vehicles can be obtained during peak power supply and power outage periods, reducing the load pressure on the power grid and retaining the emergency power supply capacity of energy storage sites;

[0035] Specifically, the power supply cycle of the regional distribution network is evenly divided into several power supply periods t′. If the average load p in the power supply period t′ is t′ Below the preset load value p av , then the power supply period is considered to be a valley period. If the average load p in the power supply period t′ t′ Higher than the preset load value p av , then the power supply period is considered to be a peak period. The optimization goal is to enable the owners of new energy electric vehicles to join the peak-shaving and valley-filling, discharge during the peak period to obtain benefits while reducing the load pressure of the distribution network, and charge during the valley period to fully utilize the power of the distribution network while improving the SOC of new energy electric vehicles. Through the peak-shifting charging and discharging behavior, the load pressure and power utilization rate of the distribution network system are increased, and the system stability is enhanced;

[0036] In order to optimize the power supply stability of the regional distribution network, the optimization objective is to minimize the mean square error of the regional distribution network load:

[0037]

[0038] Where N t′ Refers to the total number of power supply periods within the power supply cycle; P d.t′ is the distribution network load in the tth power supply period; N fb Represents the total number of distributed power sources in the region; P j.t.fb N is the discharge power of the jth distributed generation in the tth power supply period; chu Refers to the total number of energy storage sites in the region, P j.t.ch It refers to the charging power of the jth energy storage station in the tth power supply period, P j.t.dis It refers to the discharge power of the jth energy storage station in the tth power supply period; N ev represents the number of new energy electric vehicles in the region; P y.t.ev It represents the discharge power of the y-th new energy electric vehicle in the t-th power supply period; Indicates the average load during the distribution network cycle;

[0039] In order to increase the willingness of new energy electric vehicle owners to participate, an optimization goal is established to minimize the charging cost of new energy electric vehicles:

[0040]

[0041] Among them C k.t′.dis It refers to the discharge income of the yth new energy electric vehicle in the tth power supply period, C k.t′.ch It refers to the charging cost of the yth new energy electric vehicle in the tth power supply period, C k.t′.dc It refers to the battery loss cost of the y-th new energy electric vehicle in the t-th power supply period;

[0042] In order to ensure the stable operation of the power system, the network loss coefficient of the distribution network should be kept as small as possible during the charging and discharging process of new energy electric vehicles connected to the grid, so as to maintain the economic indicators of the distribution network and obtain the optimization goal of the minimum network loss of the distribution network:

[0043]

[0044] Where N m Refers to the total number of regional distribution network branches; k m It refers to the network loss cost coefficient of the mth branch; R m Refers to the line resistance of the mth branch; U ml and U mr Refers to the terminal voltage at both ends of the mth branch; Y m refers to the admittance of the mth branch;

[0045] Combining the above optimization objectives, we get the final optimization objective function:

[0046]

[0047] Among them, ω1, ω3, ω2 refer to the weight coefficients of the three objective functions; g 1.max It refers to the mean square error of period load before the distribution network system is optimized; g 2.max It refers to the electricity cost of new energy electric vehicles when they are added to the peak load shifting; g 3.max It refers to the network loss before the optimization of the distribution network system;

[0048] The constraints are the same as those in the previous steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the basic principle of the regional power supply strategy optimization method under distribution network outage. DETAILED DESCRIPTION

[0050] The present invention is described in detail below in conjunction with specific embodiments.

[0051] The regional power supply strategy optimization method under distribution network outage in this application is mainly used to consider the abnormal situation of regional power supply, and optimize the power supply mode of the regional distribution network by distributed power sources, energy storage sites and new energy charging and discharging sites, so as to ensure that temporary power replenishment is obtained as much as possible in the region to meet the power demand in key occasions and respond to emergencies.

[0052] The method for optimizing regional power supply strategy under distribution network outage in this application mainly includes the following steps:

[0053] Step 1: Establish a power supply strategy model after power outage within the regional power supply cycle T;

[0054] Establish the objective function of the best power supply effect within the regional power supply cycle T:

[0055]

[0056] Where Q(t) = Q1(t) + Q2(t) + Q3(t) refers to the regional power supply capacity;

[0057] Q1(t) refers to the discharge capacity of distributed power sources at time t; Q2(t) refers to the discharge capacity of energy storage sites at time t; Q3(t) refers to the discharge capacity of new energy electric vehicles at time t; L(t) is the load value of key regional equipment at time t;

[0058] The DG discharge capacity Q1(t) is limited by the DG output power constraint, that is, 0≤Q1(t)≤Q 1.max ;

[0059] The energy storage site discharge capacity Q2(t) is subject to site operating conditions, including:

[0060] The power constraint is

[0061] Energy conservation constraint, that is

[0062] The charge and discharge power limit constraint is

[0063] Where S0 represents the initial charge, S t.min Indicates the lower limit of the energy storage charge state at time t, S t.max Indicates the upper limit of the energy storage charge state at time t; E ch Refers to the actual storage capacity of the energy storage site; P ch (t) represents the charging rate, P dis represents the discharge rate, t2 represents the charging period of the energy storage site; t1 represents the discharging period of the energy storage site; P ch.max (t) represents the maximum charge value; P dis.max (t) represents the maximum discharge value; Q 1.max Indicates the maximum discharge capacity of distributed power supply; K ch =1 or 0 indicates the charging mark value; K dis =1 or 0 indicates the discharge standard quality; K ch +K dis =1;

[0064] The discharge capacity Q3(t) of new energy electric vehicles is constrained by the charging and discharging state of electric vehicles, that is,

[0065] Where L v (t) is the charging status of new energy electric vehicles, P iev (t) indicates that the i-th new energy electric vehicle is in the charging state; P i evd (t) indicates that the i-th new energy electric vehicle is in the discharging state;

[0066] Based on the above objective function and constraint conditions, the optimization goal is established to determine the power supply range that can meet the stable power supply within the normal power supply cycle T of the region. The power supply range conditions are then used to discharge distributed power sources, discharge new energy electric vehicles and power storage sites to meet the power load demand of key equipment in the region. On this basis, the output capacity of distributed power sources such as photovoltaics should be increased as much as possible to reduce the output dependence on new energy vehicles and energy storage sites as much as possible, make full use of the current power generation power, and retain energy storage redundancy as much as possible.

[0067] As the output party of distributed power sources, in order to improve their output willingness, the power supply strategy should be further optimized with the goal of achieving the best discharge benefit of distributed power sources, namely:

[0068] Step 2: Strategy optimization based on the best benefit of the distributed power supply discharge;

[0069] The objective function model of the optimal benefit of distributed generation discharge can be expressed as:

[0070]

[0071] Where R represents the discharge benefit of distributed power supply; C sale represents the income from the sale of electricity from distributed power sources to new energy electric vehicles; C ba is the peak-to-valley arbitrage income; C buy The cost of purchasing electricity to maintain the operation of the system; C op is the equipment operating cost; L v (t) is the charging load demand of new energy electric vehicles at time t; ρ c (t) is the unit price of distributed power supply at time t; ρ gr (t) represents the time-of-use electricity price of the distribution network at time t; P gr (t) represents the amount of electricity purchased from the distribution network at time t; P pv (t) represents the actual power supply to the region at time t; ξ pv (t) represents the generation cost of distributed generation at time t; ξ ba (t) represents the distributed generation energy storage cost at time t;

[0072] In particular, in the aforementioned steps, the discharge capacity of new energy electric vehicles is generally calculated based on the dischargeable capacity of new energy electric vehicles that have been connected in the region. However, in real life, affected by factors such as changes in road traffic conditions, the power supply willingness and behavior of electric vehicle owners will change greatly. When the road traffic conditions deteriorate and the SOC level of new energy electric vehicles decreases, it will directly lead to a decrease in the power supply willingness of new energy electric vehicle owners. Under the influence of many uncertain factors, there is great uncertainty in the actual discharge capacity of new energy electric vehicles. In order to determine the actual dischargeable capacity of electric vehicles in the region under different circumstances;

[0073] Specifically, for the two main factors that affect the discharge willingness of new energy electric vehicle owners, road traffic conditions and new energy electric vehicle SOC level, their fuzzy classifications are established respectively, including four fuzzy subsets {poor, general, relatively good, good} for representing the road traffic conditions x1 and three fuzzy subsets {insufficient, medium, sufficient} for representing the new energy electric vehicle SOC level x2;

[0074] Among them, the poor element in the road traffic status fuzzy subset indicates that the road communication status is very poor and congested, the average element indicates that the road traffic status is interfered and prone to congestion, the better element indicates that the road traffic status is good and not prone to congestion, and the good element indicates that the road traffic status is very good and not congested; the insufficient element in the new energy electric vehicle SOC level fuzzy subset indicates that the battery capacity of the new energy electric vehicle cannot meet the power demand of the next travel plan, the medium element indicates that the battery capacity of the new energy electric vehicle basically meets the power demand of the next travel plan, and the sufficient element indicates that the battery capacity of the new energy electric vehicle fully meets the power demand of the next travel plan;

[0075] The above fuzzy rules are expressed as:

[0076]

[0077] in represents the fuzzy subset corresponding to the road traffic state x1 input under the uth fuzzy rule; represents the fuzzy subset corresponding to the SOC level x2 input of the new energy electric vehicle under the uth fuzzy rule; f u (·) represents the linear function value corresponding to the u-th fuzzy rule; λ u , represents the fixed parameter of the uth fuzzy rule, u=1,2....U represents the sequence number of the fuzzy rule, and U is the total number of fuzzy rules;

[0078] Based on the TSK fuzzy rule model, the discharge willingness of new energy electric vehicles can be expressed as

[0079] in represents the membership function of the fuzzy subsets of road traffic status and the fuzzy subsets of SOC level of new energy electric vehicles;

[0080] In real life, most new energy electric vehicles spend no more than 10% of their daily trip time. Assuming that the number of electric vehicles in the region is N3, the expected discharge capacity of new energy electric vehicles at time t can be expressed as Q3(t) = θ·P 3.dis (t)·N3; where P 3.dis (t) represents the discharge power of a single new energy electric vehicle;

[0081] On the basis of the above, in order to further reduce the discharge capacity of new energy electric vehicles under regional power outages, consider using discharge price incentives to further increase the discharge willingness of new energy electric vehicle owners, obtain greater electric vehicle discharge capacity during power supply peaks and power outages, reduce the load pressure on the power grid, and retain the emergency power supply capacity of energy storage sites;

[0082] Specifically, the power supply cycle of the regional distribution network is evenly divided into several power supply periods t′. If the average load p in the power supply period t′ is t′ Below the preset load value p av , then the power supply period is considered to be a valley period. If the average load p in the power supply period t′ t′ Higher than the preset load value p av , then the power supply period is considered to be a peak period. The optimization goal is to enable the owners of new energy electric vehicles to join the peak-shaving and valley-filling, discharge during the peak period to obtain benefits while reducing the load pressure of the distribution network, and charge during the valley period to fully utilize the power of the distribution network while improving the SOC of new energy electric vehicles. Through the peak-shifting charging and discharging behavior, the load pressure and power utilization rate of the distribution network system are increased, and the system stability is enhanced;

[0083] In order to optimize the power supply stability of the regional distribution network, the optimization objective is to minimize the mean square error of the regional distribution network load:

[0084]

[0085] Where N t′ Refers to the total number of power supply periods within the power supply cycle; P d.t′ is the distribution network load in the tth power supply period; N fb Represents the total number of distributed power sources in the region; P j.t.fb N is the discharge power of the jth distributed generation in the tth power supply period; chu Refers to the total number of energy storage sites in the region, P j.t.ch It refers to the charging power of the jth energy storage station in the tth power supply period, P j.t.disIt refers to the discharge power of the jth energy storage station in the tth power supply period; N ev represents the number of new energy electric vehicles in the region; P y.t.ev It represents the discharge power of the y-th new energy electric vehicle in the t-th power supply period; Indicates the average load during the distribution network cycle;

[0086] In order to increase the willingness of new energy electric vehicle owners to participate, an optimization goal is established to minimize the charging cost of new energy electric vehicles:

[0087]

[0088] Among them C k.t′.dis It refers to the discharge income of the yth new energy electric vehicle in the tth power supply period, C k.t′.ch It refers to the charging cost of the yth new energy electric vehicle in the tth power supply period, C k.t′.dc It refers to the battery loss cost of the y-th new energy electric vehicle in the t-th power supply period;

[0089] In order to ensure the stable operation of the power system, the network loss coefficient of the distribution network should be kept as small as possible during the charging and discharging process of new energy electric vehicles connected to the grid, so as to maintain the economic indicators of the distribution network and obtain the optimization goal of the minimum network loss of the distribution network:

[0090]

[0091] Where N m Refers to the total number of regional distribution network branches; k m It refers to the network loss cost coefficient of the mth branch; R m Refers to the line resistance of the mth branch; U ml and U mr Refers to the terminal voltage at both ends of the mth branch; Y m refers to the admittance of the mth branch;

[0092] Combining the above optimization objectives, we get the final optimization objective function:

[0093]

[0094] Among them, ω1, ω3, ω2 refer to the weight coefficients of the three objective functions; g 1.max It refers to the mean square error of period load before the distribution network system is optimized; g 2.max It refers to the electricity cost of new energy electric vehicles when they are added to the peak load shifting; g 3.max It refers to the network loss before the optimization of the distribution network system;

[0095] The constraints are the same as those in the previous steps.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the essence and scope of the technical solution of the present invention.

Claims

1. A method for optimizing regional power supply strategy under distribution network outage, characterized in that: The steps include: Step 1: Establish a power supply strategy model after power outage within the regional power supply cycle T; Establish the objective function of the best power supply effect within the regional power supply cycle T Goal: Where Q(t) = Q1(t) + Q2(t) + Q3(t) refers to the regional power supply capacity; Q1(t) refers to the discharge capacity of distributed power sources at time t; Q2(t) refers to the discharge capacity of energy storage sites at time t; Q3(t) refers to the discharge capacity of new energy electric vehicles at time t; L(t) is the load value of key regional equipment at time t; The DG discharge capacity Q1(t) is limited by the DG output power constraint, that is, 0≤Q1(t)≤Q 1.max ; in The discharge capacity of the energy storage site Q2(t) is subject to the site operating conditions, including: The power constraint is Energy conservation constraint, that is The charge and discharge power limit constraint is Where S0 represents the initial charge, S t,min Indicates the lower limit of the energy storage charge state at time t, S t,max Indicates the upper limit of the energy storage charge state at time t; E ch Refers to the actual storage capacity of the energy storage site; P ch (t) represents the charging rate, P dis represents the discharge rate, t2 represents the charging period of the energy storage site; t1 represents the discharging period of the energy storage site; P ch.max (t) represents the maximum charge value; P dis.max (t) represents the maximum discharge value; Q 1.max Indicates the maximum discharge capacity of distributed power supply; K ch =1 or 0 indicates the charging mark value; K dis =1 or 0 indicates the discharge standard quality; K ch +K dis =1; The discharge capacity Q3(t) of new energy electric vehicles is constrained by the charging and discharging state of electric vehicles, that is, Where L v (t) is the charging status of new energy electric vehicles, P i ev (t) indicates that the i-th new energy electric vehicle is in the charging state; P i evd (t) indicates that the i-th new energy electric vehicle is in the discharging state; Based on the above objective function and constraint conditions, the optimization goal is established to determine the power supply range that can meet the stable power supply within the normal power supply cycle T of the region. The power supply range conditions are then used to discharge distributed power sources, discharge new energy electric vehicles and power storage sites to meet the power load demand of key equipment in the region. On this basis, the output capacity of distributed power sources such as photovoltaics should be increased as much as possible to reduce the output dependence on new energy vehicles and energy storage sites as much as possible, make full use of the current power generation power, and retain energy storage redundancy as much as possible. The power supply strategy is further optimized with the goal of achieving the best discharge benefit of distributed generation, that is, Step 2: Strategy optimization based on the best benefit of the distributed power supply discharge; The objective function model of the optimal benefit of distributed generation discharge can be expressed as: Where R represents the discharge benefit of distributed power supply; C sale represents the income from the sale of electricity from distributed power sources to new energy electric vehicles; C ba is the peak-to-valley arbitrage income; C buy The cost of purchasing electricity to maintain the operation of the system; C op is the equipment operating cost; L v (t) is the charging load demand of new energy electric vehicles at time t; ρ c (t) is the unit price of distributed power supply at time t; ρ gr (t) represents the time-of-use electricity price of the distribution network at time t; P gr (t) represents the amount of electricity purchased from the distribution network at time t; P pv (t) represents the actual power supply to the region at time t; ξ pv (t) represents the generation cost of distributed generation at time t; ξ ba (t) represents the distributed generation energy storage cost at time t.

2. The method for optimizing regional power supply strategy under power outage of distribution network according to claim 1 is characterized in that: For the two main factors that affect the discharge willingness of new energy electric vehicle owners, road traffic conditions and new energy electric vehicle SOC level, their fuzzy classifications are established respectively, including four fuzzy subsets {poor, general, relatively good, good} for representing the road traffic conditions x1 and three fuzzy subsets {insufficient, medium, sufficient} for representing the new energy electric vehicle SOC level x2; Among them, the poor element in the road traffic status fuzzy subset indicates that the road communication status is very poor and congested, the average element indicates that the road traffic status is interfered and prone to congestion, the better element indicates that the road traffic status is good and not prone to congestion, and the good element indicates that the road traffic status is very good and not congested; the insufficient element in the new energy electric vehicle SOC level fuzzy subset indicates that the battery capacity of the new energy electric vehicle cannot meet the power demand of the next travel plan, the medium element indicates that the battery capacity of the new energy electric vehicle basically meets the power demand of the next travel plan, and the sufficient element indicates that the battery capacity of the new energy electric vehicle fully meets the power demand of the next travel plan; The above fuzzy rules are expressed as: if x1 is and x2 is then in represents the fuzzy subset corresponding to the road traffic state x1 input under the uth fuzzy rule; represents the fuzzy subset corresponding to the SOC level x2 input of the new energy electric vehicle under the uth fuzzy rule; f u (·) represents the linear function value corresponding to the u-th fuzzy rule; λ u , represents the fixed parameter of the uth fuzzy rule, u=1,2....U represents the sequence number of the fuzzy rule, and U is the total number of fuzzy rules; Based on the TSK fuzzy rule model, the discharge willingness of new energy electric vehicles can be expressed as in represents the membership function of the fuzzy subsets of road traffic status and the fuzzy subsets of SOC level of new energy electric vehicles; Assuming the number of electric vehicles in the region is N3, the expected discharge capacity of new energy electric vehicles at time t can be expressed as Q3(t) = θ·P 3.dis (t)·N3; where P 3.dis (t) represents the discharge power of a single new energy electric vehicle.

3. The method for optimizing regional power supply strategy under distribution network outage according to claim 1, characterized in that: Also includes Through the discharge price incentive measures, the willingness of new energy electric vehicle owners to discharge can be further improved, and a larger discharge capacity of electric vehicles can be obtained during peak power supply and power outage periods, reducing the load pressure on the power grid and retaining the emergency power supply capacity of energy storage sites; Specifically, the power supply cycle of the regional distribution network is evenly divided into several power supply periods t′. If the average load p in the power supply period t′ is t′ Below the preset load value p av , then the power supply period is considered to be a valley period. If the average load p in the power supply period t′ t′ Higher than the preset load value p av , then the power supply period is considered to be a peak period. The optimization goal is to enable the owners of new energy electric vehicles to join the peak-shaving and valley-filling, discharge during the peak period to obtain benefits while reducing the load pressure of the distribution network, and charge during the valley period to fully utilize the power of the distribution network while improving the SOC of new energy electric vehicles. Through the peak-shifting charging and discharging behavior, the load pressure and power utilization rate of the distribution network system are increased, and the system stability is enhanced; In order to optimize the power supply stability of the regional distribution network, the optimization objective is to minimize the mean square error of the regional distribution network load: Goal1: Where N t′ Refers to the total number of power supply periods within the power supply cycle; P d.t′ is the distribution network load in the tth power supply period; N fb Represents the total number of distributed power sources in the region; P j.t.fb N is the discharge power of the jth distributed generation in the tth power supply period; chu Refers to the total number of energy storage sites in the region, P j.t.ch It refers to the charging power of the jth energy storage station in the tth power supply period, P j.t.dis It refers to the discharge power of the jth energy storage station in the tth power supply period; N ev represents the number of new energy electric vehicles in the region; P y.t.ev It represents the discharge power of the y-th new energy electric vehicle in the t-th power supply period; Indicates the average load during the distribution network cycle; In order to increase the willingness of new energy electric vehicle owners to participate, an optimization goal is established to minimize the charging cost of new energy electric vehicles: Goal2: Among them C k.t′.dis It refers to the discharge income of the yth new energy electric vehicle in the tth power supply period, C k.t′.ch It refers to the charging cost of the yth new energy electric vehicle in the tth power supply period, C k.t′.dc It refers to the battery loss cost of the y-th new energy electric vehicle in the t-th power supply period; In order to ensure the stable operation of the power system, the network loss coefficient of the distribution network should be kept as small as possible during the charging and discharging process of new energy electric vehicles connected to the grid, so as to maintain the economic indicators of the distribution network and obtain the optimization goal of the minimum network loss of the distribution network: Goal3: Where N m Refers to the total number of regional distribution network branches; k m It refers to the network loss cost coefficient of the mth branch; R m Refers to the line resistance of the mth branch; U ml and U mr Refers to the terminal voltage at both ends of the mth branch; Y m refers to the admittance of the mth branch; Combining the above optimization objectives, we get the final optimization objective function: Among them, ω1, ω3, ω2 refer to the weight coefficients of the three objective functions; g 1.max It refers to the mean square error of period load before the distribution network system is optimized; g 2.max It refers to the electricity cost of new energy electric vehicles when they are added to the peak load shifting; g 3.max It refers to the network loss before the optimization of the distribution network system; The constraints are the same as those in the previous steps.

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