CN110854891A - Power distribution network pre-disaster resource allocation method and system - Google Patents
Power distribution network pre-disaster resource allocation method and system Download PDFInfo
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Abstract
The invention provides a method and a system for allocating pre-disaster resources of a power distribution network, wherein the method comprises the following steps: establishing a sub-objective model for optimizing the safety of the power distribution network under extreme disasters and a sub-objective model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device; establishing a mobile energy storage device configuration model based on Nash negotiation according to a distribution network safety optimization sub-objective model and a distribution network economic optimization sub-objective model; and solving the mobile energy storage device configuration model to obtain an equilibrium solution of the mobile energy storage device capacity configuration. The method can meet the safety requirement and the economic requirement of the power distribution network, solves the contradiction between the safety and economic optimization sub-targets, avoids the defect that the power distribution network runs safely under the condition of small probability of extreme disasters by replacing a large amount of investment, and can greatly improve the economic benefit of normal running while improving the toughness of the power distribution network under the extreme disasters.
Description
Technical Field
The invention belongs to the technical field of power grid configuration, and particularly relates to a method and a system for pre-disaster resource configuration of a power distribution network.
Background
For planning of emergency power supply resources before a disaster, the existing research usually performs corresponding preventive measures against the influence possibly caused by an extreme disaster. In the aspect of selection of emergency power supply resources, distributed power supplies, mobile energy storage devices and the like are mainly adopted in the existing research. For example, in the research, a distributed power supply is used as an emergency power supply resource, and the optimal configuration position and capacity of the distributed power supply are solved by methods such as mixed integer programming, piecewise linearization, robust optimization and the like. But the installation position of the distributed power supply is fixed, and the distributed power supply cannot be allocated to an important load position as required in the disaster process; and the output of the distributed power supply is greatly influenced by meteorological factors, so that the stable power supply capability for important loads in the disaster process is difficult to ensure.
In addition, the existing method for planning and configuring before disaster by using emergency power supply resources only considers the toughness index of the power distribution network, only evaluates the safety index of the planning and configuring method in the prevention, survival and recovery periods, and lacks the economic measurement on the general operating scene in a normal state. Only the advantages and effects under extreme scenes are generally considered, and the economy and investment margin under normal operation are not considered. Since the probability of extreme disasters is extremely low, the method for safely operating under the scene of small probability replaced by a large amount of investment is unreasonable.
Disclosure of Invention
To overcome the existing problems or at least partially solve the problems, embodiments of the present invention provide a method and a system for allocating pre-disaster resources of a power distribution network.
According to a first aspect of an embodiment of the present invention, a method for allocating pre-disaster resources of a power distribution network is provided, including:
establishing a sub-objective model for optimizing the safety of the power distribution network under extreme disasters and a sub-objective model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device;
establishing a mobile energy storage device configuration model based on Nash negotiation according to the distribution network security optimization sub-objective model and the distribution network economic optimization sub-objective model;
and solving the mobile energy storage device configuration model, and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the sub-target model for optimizing the security of the power distribution network is as follows:
wherein, FCFor the safety benefit of the power distribution network of the mobile energy storage device, m is the total load of the power distribution network, ciA unit power outage loss for the ith load, and c1,c2,...,cmArranged from large to small according to the importance of the load, PiPower loss per blackout time for the ith load;
P∑is the total probability of load power loss within the duration of a typhoon disaster, ni(t) is the power supply recovery probability of the ith load at the moment t after the energy storage device is arranged, ttotalThe load power failure time under typhoon disaster is shown, wherein t belongs to [0, t ∈total]。
Further, wherein the total probability P of load power loss within the duration of the typhoon disaster is calculated by the following formula∑：
P∑＝Max(Pr(t)，Pl(t))+Pg+Ph(t)，t∈[0,ttotal]；
Wherein, Pr(t) is the pole failure rate at time t, Pl(t) is the failure rate at time t of the overhead line, PgIs the flashover probability, P, of a single insulatorh(t) is the fault rate of the transformer at time t; t is tbeginAt the moment of typhoon onset, tendThe boundary values of the β confidence intervals are respectively the typhoon ending timeAnd
further, wherein,
wherein, PESS(t) is the maximum discharge power which can be provided by the mobile energy storage device at the moment t, k and l are constants, PjPower loss per blackout time for the jth load;
the loads of all m power distribution networks are arranged from large to small according to the load importance degree, the loads of the 1 st to the kth power distribution networks can be completely recovered within the recovery period after the typhoon is finished, the loads of the kth to the l power distribution networks can be partially recovered within the recovery period, and the loads of the l to the m power distribution networks can not be completely recovered within the recovery period.
Further, the sub-objective model for optimizing the economy of the power distribution network is as follows:
wherein, FEFor the economic benefit of a power distribution network provided with the energy storage device, n is the charging and discharging times of the energy storage device in the operation period, PESSThe maximum charge-discharge power of the mobile energy storage device is T, the charge-discharge time corresponding to the maximum charge-discharge power is rhodischargeFor real-time electricity prices, i.e. peak-time electricity prices, ρ, when the energy storage device is dischargedchargeReal-time electricity price when charging the energy storage device, N is the maximum number of charge and discharge times of the energy storage device, CfWhich is the investment cost of the energy storage device.
Further, the objective function of the mobile energy storage device configuration model is as follows:
wherein d is1、d2Respectively, the critical values of a distribution network safety sub-target model and a distribution network economic sub-target model.
Further, solving the mobile energy storage device configuration model by using an NSGA-II algorithm, including:
a. randomly generating an energy storage device with the size of N to configure a capacity initial population, carrying out non-dominated sorting on all initial populations, and obtaining a first generation progeny population by using a genetic algorithm;
b. from the second iteration, merging the parent population and the child population, performing rapid non-dominant sorting, simultaneously performing crowding degree calculation on the individuals of each non-dominant layer, and selecting corresponding individuals to form a new parent population according to the crowding degree of the individuals of each non-dominant layer;
c. and continuously generating new child population until the iteration end condition is met to obtain the equilibrium solution of the capacity configuration of the energy storage device.
Further, the non-dominated sorting of all initial populations and the obtaining of the first generation offspring population by using the genetic algorithm includes:
the genetic algorithm utilizes binary codes to express the capacity of the energy storage device, and the decoding formula is as follows:
in the formula: bxk(k 1, 2.. times.l) constitutes the x-th section of each distribution network load, each section being l, b, longxkIs 0 or 1, TxAnd RxAre the two endpoints of the xth segment component definition domain;
taking the cross probability Pc0.6, probability of mutation PmRandomly changing the capacity of the energy storage device based on the crossover probability and the variation probability to yield a [0,1 ]]Random number r betweenand(ii) a If r isand＜PmAnd performing variation operation, and sequencing the maximum fitness of each generation of filial generation population, and decoding to obtain a first generation of filial generation population.
Further, the crowdedness of the individual of each non-dominant layer is calculated by:
wherein, L (X)i) Indicating distribution network load XiM represents the number of target models included in each distribution network load, fd max、fd minThe maximum and minimum values of the d-th object model, respectively.
According to a second aspect of the embodiments of the present invention, a system for allocating pre-disaster resources of a power distribution network is provided, including:
the first establishing module is used for establishing a sub-target model for optimizing the safety of the power distribution network under extreme disasters and a sub-target model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device;
the second establishing module is used for establishing a mobile energy storage device configuration model based on Nash negotiation according to the distribution network safety optimization sub-objective model and the distribution network economic optimization sub-objective model;
and the solving and calculating module is used for solving the mobile energy storage device configuration model and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic overall flow chart of a method for allocating pre-disaster resources of a power distribution network according to an embodiment of the present invention;
fig. 2 is a flowchart of solving a configuration model of a mobile energy storage device according to an embodiment of the present invention;
fig. 3 is a block diagram of an overall connection of a pre-disaster resource allocation system of a power distribution network according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
In an embodiment of the present invention, a method for allocating pre-disaster resources of a power distribution network is provided, and fig. 1 is a schematic overall flow chart of the method for allocating pre-disaster resources of the power distribution network provided in the embodiment of the present invention, where the method includes:
establishing a sub-objective model for optimizing the safety of the power distribution network under extreme disasters and a sub-objective model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device;
establishing a mobile energy storage device configuration model based on Nash negotiation according to the distribution network security optimization sub-objective model and the distribution network economic optimization sub-objective model;
and solving the mobile energy storage device configuration model, and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
It can be understood that the installation position of the distributed power supply in the traditional power distribution network scheme is fixed, and the distributed power supply cannot be allocated to an important load position as required in the disaster process; and the output of the distributed power supply is greatly influenced by meteorological factors, and the stable power supply capacity for important loads in the disaster process is difficult to ensure, so that the mobile energy storage device is adopted in the embodiment of the invention, and the allocation is convenient.
In addition, only a planning scheme under an extreme disaster condition or a planning scheme under normal operation is considered in the traditional power distribution network planning scheme, and under the extreme disaster condition, the power distribution scheme mainly considers the problem of safety; under normal operation, the main consideration of the power distribution scheme is the problem of economy. Most of the existing pre-disaster resource allocation methods use distributed power sources as emergency power supply resources, research on recovery after disaster by using mobile energy storage equipment is lacked, influence of pre-disaster resource allocation on normal operation economy of a power distribution network is not considered, and it is difficult to simultaneously improve toughness and normal operation economy of the power distribution network in extreme disasters.
The embodiment of the invention provides a safe and economically balanced power distribution network pre-disaster resource allocation method aiming at the defects of the traditional power distribution network scheme, and realizes the balance of safe and economic benefits in the whole life cycle of a power distribution network in an extremely-disastrous high-occurrence area by utilizing the advantages that a mobile energy storage device can be quickly transferred to an important load after a disaster occurs and has strong recovery capability. Establishing a power distribution network toughness sub-target measured by power failure loss, and obtaining the optimal capacity configuration of the mobile energy storage device by minimizing the power failure loss after extreme disasters; establishing a sub-target of the economy of the power distribution network measured by normal operation income, and obtaining the optimal capacity allocation of the mobile energy storage device by maximizing the income of the energy storage device participating in electricity price response; and comprehensively considering the sub-objectives of the safety and the economy of the power distribution network, solving the multi-objective optimization problem by using a Nash negotiation method, obtaining a balanced solution of the safety and the economic benefits, and obtaining a capacity configuration scheme of the mobile energy storage device.
On the basis of the above embodiments, in the embodiment of the present invention, under an extreme disaster condition, for example, during a typhoon disaster duration, a mobile energy storage device configuration strategy targeting security generally needs to ensure minimum power failure loss, and a corresponding power distribution network security optimization sub-target model is as follows:
wherein, FCFor the safety benefit of the power distribution network of the mobile energy storage device, m is the total load of the power distribution network, ciA unit power outage loss for the ith load, and c1,c2,...,cmArranged from large to small according to the importance of the load, PiPower loss per blackout time for the ith load;
P∑the total probability of the load power loss in the duration of the typhoon disaster can be expressed asThe sum of the pole fault rate, the overhead line fault rate, the insulator fault rate and the transformer fault rate; n isi(t) is the power supply recovery probability of the ith load at the moment t after the energy storage device is arranged, ttotalThe time of the power failure of the load under the typhoon disaster can be represented by the median of β confidence intervals of the typhoon duration, wherein t is epsilon [0, ttotal]。
On the basis of the above embodiment, in the embodiment of the invention, the total probability P of load power loss in the duration of the typhoon disaster is calculated by the following formula∑：
P∑＝Max(Pr(t)+Pl(t))+Pi+Pt，t∈[0,ttotal]； (2)
Wherein, Pr(t) is the pole failure rate at time t, Pl(t) is the failure rate at time t of the overhead line, PgIs the flashover probability, P, of a single insulatorh(t) is the fault rate of the transformer at time t; t is tbeginAt the moment of typhoon onset, tendThe boundary values of the β confidence intervals are respectively the typhoon ending time
And
on the basis of the above embodiments, in the embodiments of the present invention, wherein n is calculated by the following formulai(t)：
Wherein, PESS(t) is the maximum discharge power that the mobile energy storage device can provide at the moment t, and k and l are constants and can be calculated by the following formula:
wherein, PjPower loss per blackout time for the jth load;
the loads of all m power distribution networks are arranged from large to small according to the load importance degree, the loads of the 1 st to the kth power distribution networks can be completely recovered within the recovery period after the typhoon is finished, the loads of the kth to the l power distribution networks can be partially recovered within the recovery period, and the loads of the l to the m power distribution networks can not be completely recovered within the recovery period.
On the basis of the above embodiments, in the embodiments of the present invention, in a normal operation process of a power distribution network, a mobile energy storage device configuration strategy targeting economy generally needs to ensure that economic benefit is maximum, and a corresponding power distribution network economy optimization sub-target model is:
wherein, FEFor the economic benefit of a power distribution network provided with the energy storage device, n is the charging and discharging times of the energy storage device in the operation period, PESSThe maximum charge-discharge power of the mobile energy storage device is T, the charge-discharge time corresponding to the maximum charge-discharge power is rhodischargeFor real-time electricity prices, i.e. peak-time electricity prices, ρ, when the energy storage device is dischargedchargeReal-time electricity prices when charging the energy storage device, i.e. off-peak electricity prices; n is the maximum number of charge and discharge times of the energy storage device, CfWhich is the investment cost of the energy storage device.
Wherein, in the embodiment of the invention, N is 4000, Cf3000 yuan/kilowatt hour is taken.
Safety benefits F due to mobile energy storage deviceCAnd economic benefits FEThe method is generally an anti-adversary benefit subject, and therefore the method is used as game parties in a negotiation process, the comprehensive benefit of the game parties is defined as the product of the safety benefit and the economic benefit, the sub-target model is optimized based on the safety of the power distribution network and the sub-target model is optimized based on the economy of the power distribution network, and the mobile energy storage device configuration based on Nash negotiation is establishedThe model, its objective function is:
wherein d is1、d2Respectively, the critical values of a distribution network safety sub-target model and a distribution network economic sub-target model.
It can be understood that, aiming at the problem that each safety benefit and economic benefit in the multi-objective optimization are mutually exclusive, each sub-target is taken as a game party to participate in Nash negotiation by the Nash negotiation model, and a balanced solution with the maximum comprehensive benefit is obtained by continuously negotiating and gradually approaching the optimal front edge of Pareto. Aiming at the problems of unequal probability and different severity of consequences in a post-disaster recovery scene and a normal operation scene, linear transformation is carried out on a small-probability scene according to the linear transformation invariance axiom, and a symmetric negotiation problem is established for game parties. Aiming at the problems of different power outage losses and economic benefit dimensions, in the process of continuously approaching the optimal leading edge of Pareto, each sub-target can keep the physical significance and the dimension of the sub-target, and a balanced solution is obtained through consultation.
Referring to fig. 2, on the basis of the above embodiment, in the embodiment of the present invention, solving the mobile energy storage device configuration model by using the NSGA-ii algorithm includes:
a. randomly generating an energy storage device with the size of N to configure a capacity initial population, carrying out non-dominated sorting on all initial populations, and obtaining a first generation progeny population by using a genetic algorithm;
b. from the second iteration, merging the parent population and the child population, performing rapid non-dominant sorting, simultaneously performing crowding degree calculation on the individuals of each non-dominant layer, and selecting corresponding individuals to form a new parent population according to the crowding degree of the individuals of each non-dominant layer;
c. and continuously generating new child population until the iteration end condition is met to obtain the balanced solution of the capacity configuration of the mobile energy storage device.
On the basis of the above embodiment, in the embodiment of the present invention, performing non-dominated sorting on all initial populations, and obtaining the first generation progeny population by using a genetic algorithm includes:
it can be understood that, in the embodiment of the present invention, a first generation progeny population is obtained by using selection, intersection, variation, and the like of a genetic algorithm, where the genetic algorithm uses binary codes to represent the capacity of an energy storage device, and a decoding formula thereof is:
in the formula: bxk(k 1, 2.. times.l) constitutes the x-th section of each distribution network load, each section being l, b, longxkIs 0 or 1, TxAnd RxAre the two endpoints of the xth segment component definition domain; taking the cross probability Pc0.6, probability of mutation PmRandomly varying the capacity of the energy storage device based on the crossover probability and the mutation probability yields a [0,1 ═ 0.01]Random number r betweenand(ii) a If r isand＜PmAnd performing variation operation, and sequencing the maximum fitness of each generation of filial generation population, and decoding to obtain a first generation of filial generation population.
On the basis of the above-described embodiment, in the embodiment of the present invention, the congestion degree of each individual non-dominant layer is calculated as follows:
wherein, L (X)i) Indicating distribution network load XiM represents the number of target models included in each distribution network load, fd max、fd minThe maximum and minimum values of the d-th object model, respectively. In the embodiment of the invention, the target model comprises two target models, namely a power distribution network safety optimization target model and a power distribution network economic optimization target model.
The following describes the whole working process of the pre-disaster resource allocation method for the power distribution network provided by the embodiment of the invention.
Before establishing a distribution network security optimization sub-target model and a distribution network economic optimization sub-target model, basic information of the distribution network, including a topological structure, the importance degree of each node load, the power failure loss in unit time and the like, needs to be provided; basic information of the mobile energy storage device is required to be provided, and the basic information comprises maximum charge and discharge power, energy storage capacity, energy storage investment cost, maximum charge and discharge times and the like; meanwhile, real-time electricity price in the operation process of the power distribution network is required to be provided.
The specific work flow is as follows:
the first step is as follows: and loading original data, and respectively establishing a power distribution network security optimization sub-target model and a normal operation economy sub-target model under extreme disasters based on the mobile energy storage device.
The second step is that: and establishing a Nash negotiation model with safe economic balance. Aiming at the problem that each safety benefit and economic benefit in the multi-objective optimization are mutually exclusive, a balanced solution with the maximum comprehensive benefit is obtained by utilizing a Nash negotiation model. Aiming at the problems of unequal probability and different severity of consequences in a post-disaster recovery scene and a normal operation scene, linear transformation is carried out on a small-probability scene according to the linear transformation invariance axiom, and a symmetric negotiation problem is established for game parties; aiming at the problems of different power outage losses and economic benefit dimensions, in the process of continuously approaching the optimal leading edge of Pareto, each sub-target can keep the physical significance and the dimension of the sub-target, and a balanced solution is obtained through consultation.
The third step: randomly generating an energy storage configuration capacity initial population with the scale of N, performing non-dominated sorting, and obtaining a first generation offspring population by utilizing the operations of selection, crossing, variation and the like of a genetic algorithm; from the second iteration, combining the parent population and the offspring population, performing rapid non-dominant sorting, and simultaneously performing crowding degree calculation on the individuals of each non-dominant layer, thereby selecting proper individuals to form a new parent population; and continuously generating new child population until the iteration end condition is met to obtain the balanced solution of the capacity configuration of the mobile energy storage device.
In short, the method only needs to provide basic information of the power distribution network and the mobile energy storage device and real-time electricity price in the running process of the power distribution network, balance of safety and normal running economy of the power distribution network under extreme disasters is achieved by using a Nash negotiation method, a multi-objective optimization problem is converted into a single-objective optimization problem, a balance configuration result of the mobile energy storage device is obtained by using an NSGA-II method, and the toughness of the power distribution network is improved while the normal running economy is guaranteed.
Referring to fig. 3, in another embodiment of the present invention, a pre-disaster resource allocation system for a power distribution network is provided, which is configured to implement the methods in the foregoing embodiments. Therefore, the description and definition in each embodiment of the foregoing power distribution network pre-disaster resource allocation method may be used for understanding each execution module in the embodiment of the present invention. Fig. 3 is a schematic view of an overall structure of a pre-disaster resource allocation system of a power distribution network according to an embodiment of the present invention, where the system includes:
the first establishing module 31 is used for establishing a sub-target model for optimizing the safety of the power distribution network under the extreme disasters and a sub-target model for optimizing the economy of the power distribution network under the normal operation based on the mobile energy storage device;
the second establishing module 32 is configured to establish a mobile energy storage device configuration model based on nash negotiation according to the distribution network security optimization sub-objective model and the distribution network economic optimization sub-objective model;
and the solving and calculating module 33 is used for solving the mobile energy storage device configuration model and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
The power distribution network pre-disaster resource allocation system provided by the embodiment of the present invention corresponds to the power distribution network pre-disaster resource allocation method provided by the foregoing embodiment, and the relevant technical features of the power distribution network pre-disaster resource allocation system may refer to the relevant technical features of the power distribution network pre-disaster resource allocation method provided by the foregoing embodiment, and are not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A pre-disaster resource allocation method for a power distribution network is characterized by comprising the following steps:
establishing a sub-objective model for optimizing the safety of the power distribution network under extreme disasters and a sub-objective model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device;
establishing a mobile energy storage device configuration model based on Nash negotiation according to the distribution network security optimization sub-objective model and the distribution network economic optimization sub-objective model;
and solving the mobile energy storage device configuration model, and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
2. The method for pre-disaster resource allocation of the power distribution network according to claim 1, wherein the sub-objective model for security optimization of the power distribution network is as follows:
wherein, FCFor the safety benefit of the power distribution network of the mobile energy storage device, m is the total load of the power distribution network, ciA unit power outage loss for the ith load, and c1,c2,...,cmArranged from large to small according to the importance of the load, PiPower loss per blackout time for the ith load;
P∑is the total probability of load power loss within the duration of a typhoon disaster, ni(t) is the power supply recovery probability of the ith load at the moment t after the energy storage device is arranged, ttotalThe load power failure time under typhoon disaster is shown, wherein t belongs to [0, t ∈total]。
3. The method according to claim 2, wherein the pre-disaster resource allocation is calculated by the following formulaTotal probability P of load power loss in duration of typhoon disaster∑：
P∑＝Max(Pr(t)，Pl(t))+Pg+Ph(t)，t∈[0,ttotal]；
Wherein, Pr(t) is the pole failure rate at time t, Pl(t) is the failure rate at time t of the overhead line, PgIs the flashover probability, P, of a single insulatorh(t) is the fault rate of the transformer at time t; t is tbeginAt the moment of typhoon onset, tendThe boundary values of the β confidence intervals are respectively the typhoon ending time
And
4. the method according to claim 3, wherein the pre-disaster resource allocation of the power distribution network is performed,
wherein, PESS(t) is the maximum discharge power which can be provided by the mobile energy storage device at the moment t, k and l are constants, PjPower loss per blackout time for the jth load;
the loads of all m power distribution networks are arranged from large to small according to the load importance degree, the loads of the 1 st to the kth power distribution networks can be completely recovered within the recovery period after the typhoon is finished, the loads of the kth to the l power distribution networks can be partially recovered within the recovery period, and the loads of the l to the m power distribution networks can not be completely recovered within the recovery period.
5. The distribution network pre-disaster resource allocation method according to claim 2, wherein the distribution network economic optimization sub-target model is:
wherein, FEFor the economic benefit of a power distribution network provided with the energy storage device, n is the charging and discharging times of the energy storage device in the operation period, PESSThe maximum charge-discharge power of the mobile energy storage device is T, the charge-discharge time corresponding to the maximum charge-discharge power is rhodischargeFor real-time electricity prices, i.e. peak-time electricity prices, ρ, when the energy storage device is dischargedchargeReal-time electricity price when charging the energy storage device, N is the maximum number of charge and discharge times of the energy storage device, CfWhich is the investment cost of the energy storage device.
6. The method for pre-disaster resource allocation of a power distribution network according to claim 5, wherein the objective function of the mobile energy storage device allocation model is as follows:
wherein d is1、d2Respectively, the critical values of a distribution network safety sub-target model and a distribution network economic sub-target model.
7. The method for pre-disaster resource allocation of the power distribution network according to claim 6, wherein solving the mobile energy storage device allocation model by using an NSGA-II algorithm comprises:
a. randomly generating an energy storage device with the size of N to configure a capacity initial population, carrying out non-dominated sorting on all initial populations, and obtaining a first generation progeny population by using a genetic algorithm;
b. from the second iteration, merging the parent population and the child population, performing rapid non-dominant sorting, simultaneously performing crowding degree calculation on the individuals of each non-dominant layer, and selecting corresponding individuals to form a new parent population according to the crowding degree of the individuals of each non-dominant layer;
c. and continuously generating new child population until the iteration end condition is met to obtain the equilibrium solution of the capacity configuration of the energy storage device.
8. The method according to claim 7, wherein the non-dominated sorting of all the initial populations and the obtaining of the first generation offspring population by using the genetic algorithm comprises:
the genetic algorithm utilizes binary codes to express the capacity of the energy storage device, and the decoding formula is as follows:
in the formula: bxk(k 1,2, …, l) forming the x-th section of each distribution network load, each section being l, bxkIs 0 or 1, TxAnd RxAre the two endpoints of the xth segment component definition domain;
taking the cross probability Pc0.6, probability of mutation PmRandomly changing the capacity of the energy storage device based on the crossover probability and the variation probability to yield a [0,1 ]]Random number r betweenand(ii) a If r isand＜PmAnd performing variation operation, and sequencing the maximum fitness of each generation of filial generation population, and decoding to obtain a first generation of filial generation population.
9. The method according to claim 7, wherein the congestion level of each individual non-dominated layer is calculated as follows:
wherein, L (X)i) Indicating distribution network load XiM represents the number of target models included in each distribution network load,
the maximum and minimum values of the d-th object model, respectively.
10. A pre-disaster resource allocation system for a power distribution network is characterized by comprising:
the first establishing module is used for establishing a sub-target model for optimizing the safety of the power distribution network under extreme disasters and a sub-target model for optimizing the economy of the power distribution network under normal operation based on the mobile energy storage device;
the second establishing module is used for establishing a mobile energy storage device configuration model based on Nash negotiation according to the distribution network safety optimization sub-objective model and the distribution network economic optimization sub-objective model;
and the solving and calculating module is used for solving the mobile energy storage device configuration model and calculating to obtain an equilibrium solution of the mobile energy storage device capacity configuration.
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