Localized emergency power supply charging pile transformation optimization method, system and related device

Abstract

The invention discloses a local emergency power supply and charging pile transformation optimization method and system and a related device, and the method comprises the steps: considering the distribution of building-level electric vehicles and the uncertainty of loads, considering the estimation of the occurrence frequency of disasters, and determining a disaster scene set; defining an operation complexity index by combining economic loss and operation cost brought by charge and discharge state conversion of the building-level electric vehicle; taking the sum of the operation, maintenance and transformation cost of the charging station, the scheduling cost of the building-level electric vehicle, the economic cost loss caused by load power loss and the conversion cost of the operation complexity index as an objective function of the charging pile transformation optimization model; and performing linearization processing on the charging pile transformation optimization model, and performing solution by considering the objective function and the constraint condition under the disaster scene set by using the linearized charging pile transformation optimization model to obtain a charging pile transformation optimization scheme. According to the invention, a reasonable optimization scheme can be formulated for the transformation of the bidirectional charging and discharging interface of the charging pile for the post-disaster building localization emergency power supply recovery.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency power supply, and specifically relates to a localized emergency power supply charging pile transformation optimization method, system and related devices. Background Art

[0002] To restore local emergency power supply to buildings after a disaster, it is necessary to conduct a comprehensive assessment of the existing power supply facilities in the disaster area to understand the severity of the power outage, the degree of damage, and the difficulty of restoration. The transformation of the bidirectional charging and discharging interface of the charging piles of building-level electric vehicles is a feasible means to achieve local emergency power supply. The V2B (Vehicle to Building) model is to apply bidirectional inverter charging and discharging technology to commercial buildings, mainly to solve the problem of power balance in the power grid during peak and trough periods of power consumption. In the V2B model, electric vehicles are equivalent to small distributed power stations.

[0003] The planning of the transformation of the bidirectional charging and discharging interface of charging piles needs to evaluate the distribution, type, damage and remaining capacity of electric vehicle charging piles in the disaster area, and predict the total amount and time period of emergency power supply according to the power demand of residents in the disaster area, temporary resettlement sites, rescue teams, etc. At the same time, considering the long-term power supply demand in the post-disaster reconstruction process, a phased emergency power supply restoration plan should be formulated.

[0004] During the implementation process, the existing charging piles in the disaster area will be surveyed on site to understand their quantity, distribution, damage and remaining capacity. The feasibility and economic efficiency of the charging pile transformation will be evaluated, and a detailed transformation plan will be formulated. The transformed charging piles will also be debugged and tested to ensure that they have bidirectional charging and discharging functions and stable operation. Emergency power supply simulation tests will also be conducted to verify the actual effect of the charging piles in emergency power supply restoration. In order to achieve efficient use and reasonable allocation of electric energy and improve the efficiency of emergency power supply restoration, it is necessary to optimize the charging strategy of the charging piles according to the actual power demand and grid load conditions in the disaster area.

[0005] In recent years, with the continuous maturity of electric vehicle technology and the reduction of costs, the building-level electric vehicle market has shown a rapid growth trend. The distribution of building-level electric vehicles is affected by many factors, including economic development level, policy support, infrastructure construction, etc. At the same time, the charging demand of building-level electric vehicles is affected by many factors, such as vehicle type, battery capacity, frequency of use, charging time, etc. These factors lead to greater uncertainty in the charging demand of electric vehicles. Especially in complex environments such as construction sites, the charging demand of electric vehicles may be affected by multiple factors such as project progress and vehicle scheduling, which further increases the uncertainty of load. The charging behavior of electric vehicle users also has certain uncertainties. For example, users may choose charging time and charging location based on their own vehicle needs, the availability of charging facilities, charging prices and other factors. This uncertainty may cause fluctuations in charging load and peak-to-valley differences, which will have a certain impact on the stable operation of the power grid. There is a mutual influence between the charging load of building-level electric vehicles and the load of the power grid. When a large number of electric vehicles are charged at the same time, it may cause a sharp increase in the load of the power grid, and even cause risks such as power grid overload and power outage. Therefore, when planning and managing building-level electric vehicle charging facilities, it is necessary to fully consider the load bearing capacity of the power grid and formulate reasonable charging strategies and scheduling plans. For example, orderly charging and staggered charging can be used to balance the relationship between grid load and charging demand; at the same time, energy storage systems and other means can be used to smooth out fluctuations in charging load and peak-to-valley differences. Summary of the invention

[0006] The purpose of the present invention is to provide a localized emergency power supply charging pile transformation optimization method, system and related devices to address the problems in the above-mentioned prior art, taking into account the uncertainty of building-level electric vehicle distribution and load, the frequency estimation of disasters, and the economic losses and operating costs caused by electric vehicle state switching, and formulate a reasonable charging pile transformation optimization plan.

[0007] In order to achieve the above object, the present invention has the following technical solutions:

[0008] In a first aspect, a localized emergency power supply charging pile transformation optimization method is provided, comprising:

[0009] Considering the building-level electric vehicle distribution and load uncertainty, taking into account the frequency estimation of disasters, a set of disaster scenarios is determined;

[0010] The operational complexity index is defined by combining the economic losses and operational costs caused by the charging and discharging state conversion of building-level electric vehicles;

[0011] The sum of the operation and maintenance and renovation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost loss caused by load power failure, and the converted costs of the operation complexity index is used as the objective function of the pre-established charging pile renovation optimization model;

[0012] The charging pile transformation optimization model is linearized, and the charging pile transformation optimization model after linearization is used to solve the disaster scenario set, considering the objective function and constraints, and the charging pile transformation optimization plan is obtained.

[0013] As a preferred solution, the step of determining a disaster scenario set by taking into account the building-level electric vehicle distribution and load uncertainty and the frequency estimation of disaster occurrence includes: determining the time limit and the number of disaster occurrences based on historical statistical data, and obtaining the time and frequency of each disaster; and determining the number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle based on a pre-established building-level electric vehicle distribution model, and determining the load demand during the duration of the disaster based on a pre-established typical daily load distribution model for the building; and constructing the disaster scenario set required for the charging pile transformation optimization model based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the duration of the disaster.

[0014] As a preferred solution, in the step of defining the operation complexity index in combination with the economic loss and operation cost caused by the charging and discharging state conversion of the building-level electric vehicle, the expression for defining the operation complexity index is as follows:

[0015]

[0016] In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

[0017] As a preferred solution, in the step of taking the sum of the operation and maintenance and transformation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost losses caused by load power failure, and the converted costs of the operation complexity index as the objective function of the pre-established charging pile transformation optimization model, F1 represents the operation and maintenance and transformation costs of the charging station, and F2 represents the sum of the dispatching costs of building-level electric vehicles in various scenarios; F3 represents the sum of the economic cost losses caused by load power failure in various scenarios, and F4 represents the converted costs of the operation complexity index:

[0018] F=min{F1+F2+F3+F4}

[0019] F1=Ccs ·N cs

[0020]

[0021]

[0022] Among them, N cs is an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of remodeling a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ΔT is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

[0023] As a preferred solution, the constraints of the charging pile transformation optimization model include:

[0024] The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows:

[0025]

[0026] Where P i t is the power supply of the i-th building-level electric vehicle at time t, The total power supply for building-level electric vehicles, is the maximum power supply allowed at time t;

[0027] The number of charging piles to be built is N cs , the maximum power that can be transmitted is:

[0028]

[0029] The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity:

[0030]

[0031] The constraint on the remaining power of a single electric vehicle is:

[0032] W i t =W i t-1 -ΔW i t ,

[0033] It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is:

[0034]

[0035] Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, W i t is the remaining power of a single building-level electric vehicle at time t under scenario s, W i t-1 is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, ΔW i t is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

[0036] As a preferred solution, in the step of linearizing the charging pile transformation optimization model, an auxiliary binary variable is introduced Add the following constraints:

[0037]

[0038] Equivalent to

[0039] Substituting into the expression that defines the operation complexity index, we get:

[0040]

[0041] The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

[0042] In the second aspect, a localized emergency power supply charging pile transformation and optimization system is provided, including:

[0043] The disaster scenario set establishment module is used to determine the disaster scenario set by considering the building-level electric vehicle distribution and load uncertainty, taking into account the disaster frequency estimation;

[0044] An operation complexity index definition module, used to define the operation complexity index by combining the economic losses and operation costs caused by the charging and discharging state conversion of building-level electric vehicles;

[0045] A charging pile transformation optimization model objective function establishment module is used to use the sum of the operation and maintenance and transformation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost losses caused by load power failure, and the converted costs of the operation complexity index as the objective function of the pre-established charging pile transformation optimization model;

[0046] The charging pile transformation optimization model solving module is used to linearize the charging pile transformation optimization model, and use the charging pile transformation optimization model after linearization to solve the disaster scenario set, considering the objective function and constraints, and obtain the charging pile transformation optimization plan.

[0047] As a preferred solution, the disaster scenario set establishment module determines the time limit and number of disaster occurrences based on historical statistical data, and obtains the time and frequency of each disaster; and determines the number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle based on a pre-established building-level electric vehicle distribution model, and determines the load demand during the duration of the disaster based on a pre-established typical daily load distribution model for the building; and constructs the disaster scenario set required for the charging pile transformation optimization model based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the duration of the disaster.

[0048] As a preferred solution, the operation complexity index definition module defines the expression of the operation complexity index as follows:

[0049]

[0050] In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

[0051] As a preferred solution, the charging pile transformation optimization model objective function establishment module uses F1 to represent the operation and maintenance and transformation costs of the charging station, and F2 to represent the sum of the dispatching costs of building-level electric vehicles in various scenarios; F3 is the sum of the economic cost losses caused by load power failure in various scenarios, and F4 represents the converted cost of the operation complexity index. The objective function of the charging pile transformation optimization model is as follows:

[0052] F=min{F1+F2+F3+F4}

[0053] F1=C cs ·N cs

[0054]

[0055] Among them, N cs is an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of remodeling a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ΔT is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

[0056] As a preferred solution, when the charging pile transformation optimization model solving module considers the objective function and the constraint conditions to solve the charging pile transformation optimization model, the constraint conditions of the charging pile transformation optimization model include:

[0057] The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows:

[0058]

[0059] Where P i tis the power supply of the i-th building-level electric vehicle at time t, is the total power supply for building-level electric vehicles, is the maximum power supply allowed at time t;

[0060] The number of charging piles to be built is N cs , the maximum power that can be transmitted is:

[0061]

[0062] The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity:

[0063]

[0064] The constraint on the remaining power of a single electric vehicle is:

[0065] W i t =W i t-1 -ΔW i t ,

[0066] It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is:

[0067]

[0068] Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, W i t is the remaining power of a single building-level electric vehicle at time t under scenario s, W i t-1 is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, ΔW i t is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

[0069] As a preferred solution, the charging pile transformation optimization model solving module introduces an auxiliary binary variable when linearizing the charging pile transformation optimization model. Add the following constraints:

[0070]

[0071] Equivalent to

[0072] Substituting into the expression that defines the operation complexity index, we get:

[0073]

[0074] The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

[0075] In a third aspect, an electronic device is provided, characterized in that it includes a processor and a memory, and the processor is used to execute a computer program stored in the memory to implement the localized emergency power supply charging pile transformation and optimization method.

[0076] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the localized emergency power supply charging pile transformation and optimization method is implemented.

[0077] Compared with the prior art, the first aspect of the present invention has at least the following beneficial effects:

[0078] The present invention proposes a planning method for the reconstruction of the bidirectional charging and discharging interface of charging piles for localized emergency power supply restoration of buildings after disasters. In the design process of the optimization scheme for the reconstruction of charging piles, the uncertainty of the distribution of building-level electric vehicles and loads is taken into account, and the frequency estimation of disasters is taken into account to generate a set of disaster scenarios used for optimization. The economic losses and operating costs caused by the state switching of electric vehicles are taken into account, and based on this, the definition of the operation complexity index is proposed. At the same time, with the goal of minimizing economic costs, a two-stage charging pile reconstruction optimization model is established. The localized emergency power supply charging pile reconstruction optimization method of the present invention is oriented to the application scenarios of future building-level electric vehicles, and has strong technical foresight. In addition, the uncertainty of disasters and the uncertainty of the distribution of building-level electric vehicles are taken into account, and the modeling considerations are relatively sufficient. The present invention can provide theoretical support for the future research on the use of electric vehicles for localized emergency power supply restoration at the building level, thereby improving the resilience of the power system.

[0079] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0081] Figure 1 A schematic diagram of a process flow of a localized emergency power supply charging pile modification optimization method according to an embodiment of the present invention;

[0082] Figure 2 A schematic diagram of a process for determining a disaster scenario set according to an embodiment of the present invention. DETAILED DESCRIPTION

[0083] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0084] See also Figure 1 The local emergency power supply charging pile transformation optimization method of the embodiment of the present invention includes the following steps:

[0085] Considering the building-level electric vehicle distribution and load uncertainty, taking into account the frequency estimation of disasters, a set of disaster scenarios is determined;

[0086] The operational complexity index is defined by combining the economic losses and operational costs caused by the charging and discharging state conversion of building-level electric vehicles;

[0087] The sum of the operation and maintenance and renovation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost loss caused by load power failure, and the converted costs of the operation complexity index is used as the objective function of the pre-established charging pile renovation optimization model;

[0088] The charging pile transformation optimization model is linearized, and the charging pile transformation optimization model after linearization is used to solve the disaster scenario set, considering the objective function and constraints, and the charging pile transformation optimization plan is obtained.

[0089] In a certain period of time, there are no available building-level electric vehicles near the building. Therefore, during the planning period, the main consideration is that the disaster occurs during the period when people are more active, that is, there are a certain number of available building-level electric vehicles around the building to participate in disaster recovery power supply. Figure 2In a possible implementation, the step of determining a disaster scenario set by considering building-level electric vehicle distribution and load uncertainty and taking into account disaster frequency estimation includes:

[0090] According to the scenario sampling method and based on historical statistical data, the time limit and number of disaster occurrences are determined to obtain the time and frequency of each disaster. The number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle are determined based on the pre-established building-level electric vehicle distribution model. The load demand during the duration of the disaster is determined based on the pre-established typical daily load distribution model of the building. The disaster scenario set required for the charging pile transformation optimization model is constructed based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the duration of the disaster.

[0091] The disaster scenario generation method of the embodiment of the present invention uses the Monte Carlo method, which is also called the statistical simulation method and the statistical test method. It is a numerical simulation method that takes probability phenomena as the research object, and is a calculation method that obtains statistical values ​​according to the sampling survey method to infer unknown characteristic quantities. The essence of the Monte Carlo method is a random sampling method, which is suitable for computational simulation tests on discrete systems. In computational simulation, by constructing a probability model that is similar to the system performance and conducting random tests on a digital computer, the random characteristics of the system can be simulated. In order to solve the problem, the Monte Carlo method first establishes a probability model or a random process so that its parameters or digital features are equal to the solution of the problem: then these parameters or digital features are calculated by observing or sampling the model or process, and finally the approximate value of the solution is given. The accuracy of the solution is expressed by the standard error of the estimated value. The main theoretical basis of the Monte Carlo method is the probability and statistical theory, and the main means are random sampling and statistical tests. The basic steps of the Monte Carlo method for solving practical problems are: (1) Based on the characteristics of the practical problem, construct a simple and easy-to-implement probability statistical model so that the solution is exactly the probability distribution or mathematical expectation of the problem; (2) Provide a sampling method for various random variables with different distributions in the model; (3) Statistically process the simulation results and provide a statistical estimate and precision estimate of the solution to the problem.

[0092] In a possible implementation, considering the impact of the charging and discharging state conversion of building-level electric vehicles on power supply at each moment and the time loss caused by the operation interval, the embodiment of the present invention defines the expression of the operation complexity index for a given sequential power supply strategy as follows:

[0093]

[0094] In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

[0095] In one possible implementation, the charging pile transformation optimization model established by the embodiment of the present invention belongs to a stochastic programming model. The stochastic programming model is used to study linear programming problems when the coefficients in the constraints and the parameters in the objective function are all random variables, and is used to study decision-making problems with uncertainty. The central problem of stochastic programming is to select parameters to maximize the mathematical expectation of the benefits or minimize the mathematical expectation of the costs. Stochastic programming is a theory and method that includes random variables in mathematical programming models. The optimal solution to the problem can be obtained based on the mathematical model, but this optimal solution is generally not a definite value but an expected value. In stochastic programming, it is necessary to describe the random variables and analyze their probability distribution. It is often necessary to consider the autocorrelation and cross-correlation of each random variable. Therefore, it is much more complicated than deterministic programming in theory and solution methods.

[0096] In fact, when solving a stochastic programming problem, we always try to convert it into a deterministic mathematical programming problem before solving it. If the uncertainty or quantitative change of the random variable is small and does not have a serious impact on the performance of the system, we can replace this uncertain value with its mathematical expectation and solve it using a deterministic method. Then, we can use sensitivity analysis to evaluate the impact of uncertain factors on the solution. If the random variable changes greatly, using the expected value may greatly affect the evaluation of the solution performance, and then we need to use a stochastic programming method to solve it.

[0097] The embodiment of the present invention uses the sum of the operation and maintenance and transformation costs of the charging station, the dispatching cost of building-level electric vehicles, the economic cost loss caused by load power failure, and the converted cost of the operation complexity index as the objective function of the charging pile transformation optimization model.

[0098] F1 represents the operation and maintenance and transformation costs of the charging station, F2 represents the sum of the dispatching costs of building-level electric vehicles in each scenario; F3 represents the sum of the economic cost losses caused by load power failure in each scenario, and F4 represents the conversion cost of the operation complexity index. The objective function expression of the charging pile transformation optimization model is as follows:

[0099] F=min{F1+F2+F3+F4}

[0100] F1=C cs ·N cs

[0101]

[0102] Among them, N csis an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of remodeling a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ×T is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

[0103] Furthermore, the constraints of the charging pile transformation optimization model include:

[0104] The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows:

[0105]

[0106] Where P i t is the power supply of the i-th building-level electric vehicle at time t, is the total power supply for building-level electric vehicles, is the maximum power supply allowed at time t;

[0107] The number of charging piles to be built is N cs , the maximum power that can be transmitted is:

[0108]

[0109] The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity:

[0110]

[0111] The constraint on the remaining power of a single electric vehicle is:

[0112] W i t =Wi t-1 -ΔW i t ,

[0113] It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is:

[0114]

[0115] Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, W i t is the remaining power of a single building-level electric vehicle at time t under scenario s, W i t-1 is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, ΔW i t is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

[0116] Furthermore, there is a unique nonlinear term in the expression defining the operational complexity index Therefore, in the step of linearizing the charging pile transformation optimization model, an auxiliary binary variable is introduced Add the following constraints:

[0117]

[0118] Equivalent to

[0119] Substituting into the expression that defines the operation complexity index, we get:

[0120]

[0121] The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

[0122] The present invention proposes a planning method for the reconstruction of the bidirectional charging and discharging interface of charging piles for localized emergency power supply restoration of buildings after disasters. Taking into account the uncertainty of building-level electric vehicle distribution and load, and taking into account the frequency estimation of disasters, a set of disaster scenarios used for optimization is generated. The economic losses and operating costs caused by the state switching of electric vehicles are considered, and based on this, the definition of the operation complexity index is proposed. And with the goal of minimizing the economic cost, a two-stage optimization model is established. The present invention provides theoretical support for future research on the use of electric vehicles for building-level localized emergency power supply restoration, and improves the resilience of the power system.

[0123] Another embodiment of the present invention further provides a localized emergency power supply charging pile transformation and optimization system, including:

[0124] The disaster scenario set establishment module is used to determine the disaster scenario set by considering the building-level electric vehicle distribution and load uncertainty, taking into account the disaster frequency estimation;

[0125] An operation complexity index definition module, used to define the operation complexity index by combining the economic losses and operation costs caused by the charging and discharging state conversion of building-level electric vehicles;

[0126] A charging pile transformation optimization model objective function establishment module is used to use the sum of the operation and maintenance and transformation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost losses caused by load power failure, and the converted costs of the operation complexity index as the objective function of the pre-established charging pile transformation optimization model;

[0127] The charging pile transformation optimization model solving module is used to linearize the charging pile transformation optimization model, and use the charging pile transformation optimization model after linearization to solve the disaster scenario set, considering the objective function and constraints, and obtain the charging pile transformation optimization plan.

[0128] In one possible implementation, the disaster scenario set establishment module determines the time limit and number of disaster occurrences based on historical statistical data, and obtains the time and frequency of each disaster; and determines the number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle based on a pre-established building-level electric vehicle distribution model, and determines the load demand during the duration of the disaster based on a pre-established typical daily load distribution model for the building; and constructs the disaster scenario set required for the charging pile transformation optimization model based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the duration of the disaster.

[0129] In a possible implementation, the operation complexity index definition module defines an expression of the operation complexity index as follows:

[0130]

[0131] In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

[0132] In a possible implementation, the charging pile transformation optimization model objective function establishment module uses F1 to represent the operation and maintenance and transformation costs of the charging station, and F2 to represent the sum of the dispatching costs of building-level electric vehicles in various scenarios; F3 is the sum of the economic cost losses caused by load power failure in various scenarios, and F4 represents the converted cost of the operation complexity index. The objective function of the charging pile transformation optimization model is as follows:

[0133] F=min{F1+F2+F3+F4}

[0134] F1=C cs ·N cs

[0135]

[0136]

[0137] Among them, N cs is an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of remodeling a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ΔT is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

[0138] In a possible implementation, when the charging pile transformation optimization model solving module considers the objective function and the constraint conditions to solve the charging pile transformation optimization model, the constraint conditions of the charging pile transformation optimization model include:

[0139] The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows:

[0140]

[0141] Where P i t is the power supply of the i-th building-level electric vehicle at time t, is the total power supply for building-level electric vehicles, is the maximum power supply allowed at time t;

[0142] The number of charging piles to be built is N cs , the maximum power that can be transmitted is:

[0143]

[0144] The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity:

[0145]

[0146] The constraint on the remaining power of a single electric vehicle is:

[0147] W i t =W i t-1 -ΔW i t ,

[0148] It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is:

[0149]

[0150] Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, W i t is the remaining power of a single building-level electric vehicle at time t under scenario s, W i t-1is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, ΔW i t is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

[0151] In a possible implementation manner, when the charging pile transformation optimization model solving module performs linearization processing on the charging pile transformation optimization model, an auxiliary binary variable is introduced Add the following constraints:

[0152]

[0153] Equivalent to

[0154] Substituting into the expression that defines the operation complexity index, we get:

[0155]

[0156] The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

[0157] Another embodiment of the present invention further proposes an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the localized emergency power supply charging pile modification optimization method as described.

[0158] Another embodiment of the present invention further proposes a computer-readable storage medium, which stores at least one instruction. When the at least one instruction is executed by a processor, the localized emergency power supply charging pile transformation and optimization method is implemented.

[0159] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. For ease of explanation, the above content only shows the part related to the embodiment of the present invention. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium is non-temporary and can be stored in a storage device formed by various electronic devices, which can realize the execution process recorded in the method of the embodiment of the present invention.

[0160] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A localized emergency power supply charging pile transformation optimization method, characterized in that: include: Considering the building-level electric vehicle distribution and load uncertainty, taking into account the frequency estimation of disasters, a set of disaster scenarios is determined; The operational complexity index is defined by combining the economic losses and operational costs caused by the charging and discharging state conversion of building-level electric vehicles; The sum of the operation and maintenance and renovation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost loss caused by load power failure, and the converted costs of the operation complexity index is used as the objective function of the pre-established charging pile renovation optimization model; The charging pile transformation optimization model is linearized, and the charging pile transformation optimization model after linearization is used to solve the disaster scenario set, considering the objective function and constraints, and the charging pile transformation optimization plan is obtained.

2. The localized emergency power supply charging pile transformation optimization method according to claim 1 is characterized in that: The steps of determining a disaster scenario set by considering the distribution of building-level electric vehicles and load uncertainty and taking into account the estimation of the frequency of disaster occurrence include: determining the time limit and the number of disaster occurrences based on historical statistical data, and obtaining the time and frequency of each disaster; and determining the number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle based on a pre-established building-level electric vehicle distribution model, and determining the load demand during the duration of the disaster based on a pre-established typical daily load distribution model for the building; and constructing a disaster scenario set required for a charging pile transformation optimization model based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the duration of the disaster.

3. The localized emergency power supply charging pile transformation optimization method according to claim 1 is characterized in that: In the step of defining the operation complexity index in combination with the economic loss and operation cost caused by the charging and discharging state conversion of the building-level electric vehicle, the expression for defining the operation complexity index is as follows: In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

4. The localized emergency power supply charging pile transformation optimization method according to claim 3 is characterized in that: In the step of using the sum of the operation and maintenance and transformation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost loss caused by load power failure, and the converted costs of the operation complexity index as the objective function of the pre-established charging pile transformation optimization model, F1 represents the operation and maintenance and transformation costs of the charging station, and F2 represents the sum of the dispatching costs of building-level electric vehicles in each scenario; F3 is the sum of the economic cost losses caused by load power failure in each scenario, and F4 is the converted cost of the operation complexity index: F=min{F1+F2+F3+F4} F1=C cs ·N cs Among them, N cs is an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of transforming a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ΔT is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

5. The localized emergency power supply charging pile transformation optimization method according to claim 4 is characterized in that: The constraints of the charging pile transformation optimization model include: The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows: In the formula, is the power supply of the i-th building-level electric vehicle at time t, is the total power supply for building-level electric vehicles, is the maximum power supply allowed at time t; The number of charging piles to be built is N cs , the maximum power that can be transmitted is: The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity: The constraint on the remaining power of a single electric vehicle is: It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is: Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of a single building-level electric vehicle at time t under scenario s, is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

6. The localized emergency power supply charging pile transformation optimization method according to claim 5 is characterized in that: In the step of linearizing the charging pile transformation optimization model, an auxiliary binary variable is introduced Add the following constraints: Equivalent to Substituting into the expression that defines the operation complexity index, we get: The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

7. A localized emergency power supply charging pile transformation and optimization system, characterized in that: include: The disaster scenario set establishment module is used to determine the disaster scenario set by considering the building-level electric vehicle distribution and load uncertainty, taking into account the frequency estimation of disasters; An operation complexity index definition module, used to define the operation complexity index by combining the economic losses and operation costs caused by the charging and discharging state conversion of building-level electric vehicles; A charging pile transformation optimization model objective function establishment module is used to use the sum of the operation and maintenance and transformation costs of the charging station, the dispatching costs of building-level electric vehicles, the economic cost losses caused by load power failure, and the converted costs of the operation complexity index as the objective function of the pre-established charging pile transformation optimization model; The charging pile transformation optimization model solving module is used to linearize the charging pile transformation optimization model, and use the charging pile transformation optimization model after linearization to solve the disaster scenario set, considering the objective function and constraints, and obtain the charging pile transformation optimization plan.

8. The localized emergency power supply charging pile transformation and optimization system according to claim 7 is characterized in that: The disaster scenario set establishment module determines the time limit and number of disaster occurrences based on historical statistical data, and obtains the time and frequency of each disaster; and determines the number of building-level electric vehicles available for disasters and the battery state of charge (SOC) of each building-level electric vehicle based on a pre-established building-level electric vehicle distribution model, and determines the load demand during the disaster duration based on a pre-established typical daily load distribution model for the building; and constructs the disaster scenario set required for the charging pile transformation optimization model based on the battery state of charge (SOC) of each building-level electric vehicle and the load demand during the disaster duration.

9. The localized emergency power supply charging pile transformation and optimization system according to claim 7 is characterized in that: The expression of the operation complexity index defined by the operation complexity index definition module is as follows: In the formula, is the power supply status of the i-th building-level electric vehicle at time t, is the power supply status of the i-th building-level electric vehicle at time t-1, T is the total planning period; N(t s ) represents the total number of vehicles that can participate in power supply under scenario s.

10. The localized emergency power supply charging pile transformation and optimization system according to claim 9 is characterized in that: The objective function establishment module of the charging pile transformation optimization model uses F1 to represent the operation and maintenance and transformation costs of the charging station, and uses F2 to represent the sum of the scheduling costs of building-level electric vehicles in various scenarios; With F3 as the sum of the economic cost losses caused by load power failure in each scenario, and F4 as the converted cost of the operation complexity index, the objective function of the charging pile transformation optimization model is as follows: F=min{F1+F2+F3+F4} F1=C cs ·N cs Among them, N cs is an integer decision variable, indicating the number of bidirectional charging and discharging piles to be built; is a 0-1 variable, indicating the state of vehicle i in scenario s participating in power supply during the tth time period, 0 means not participating in power supply, and 1 means participating in power supply; C cs is the cost of transforming a single charging pile, C vc is the cost of dispatching a car; p s is the probability corresponding to scene s; N v (t s ) is the total number of building-level electric vehicles available under scenario s, is the unpowered load under scenario s; S is the set of disaster power supply scenarios; C load is the economic loss per unit of electricity, N s is the frequency of disasters during the planning period; ΔT is the time interval; C cpl CPL is the conversion coefficient of the operation complexity index; s To define the operation complexity index.

11. The localized emergency power supply charging pile transformation and optimization system according to claim 10 is characterized in that: When the charging pile transformation optimization model solving module solves the charging pile transformation optimization model by considering the objective function and the constraint conditions, the constraint conditions of the charging pile transformation optimization model include: The total transmission power of building-level electric vehicles shall not exceed the upper limit of the transmission power, and shall not be less than the power P of the building's basic power load. base , considering the constraints in a single scenario, it is known that the disaster occurs at t s The expression for a single scene s is as follows: In the formula, is the power supply of the i-th building-level electric vehicle at time t, is the total power supply for building-level electric vehicles, is the maximum power supply allowed at time t; The number of charging piles to be built is N cs , the maximum power that can be transmitted is: The discharge capacity of a single electric vehicle within ΔT time is less than or equal to the remaining capacity: The constraint on the remaining power of a single electric vehicle is: It means that the discharge power of a single electric vehicle at time t does not exceed the power upper limit, and the expression is: Where P b (t) is the building load at time t, P cs is the transformation cost of a single charging pile, t s is the time when the power supply cycle starts, N is the duration of the disaster, is the power supply of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of the i-th building-level electric vehicle at time t under scenario s, is the remaining power of a single building-level electric vehicle at time t under scenario s, is the remaining power of a single building-level electric vehicle at time t-1 under scenario s, is the remaining power consumed by a single building-level electric vehicle at time t under scenario s, P max It is the maximum discharge power of a single building-level electric vehicle.

12. The localized emergency power supply charging pile transformation and optimization system according to claim 11 is characterized in that: When the charging pile transformation optimization model solving module performs linear processing on the charging pile transformation optimization model, an auxiliary binary variable is introduced. Add the following constraints: Equivalent to Substituting into the expression that defines the operation complexity index, we get: The charging pile transformation optimization model after linearization processing is a mixed integer programming model.

13. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the localized emergency power supply charging pile transformation optimization method as described in any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the localized emergency power supply charging pile transformation and optimization method according to any one of claims 1 to 6.

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