CN111651899A - Robust site selection and volume determination method and system for power conversion station considering user selection behavior - Google Patents

Robust site selection and volume determination method and system for power conversion station considering user selection behavior Download PDF

Info

Publication number
CN111651899A
CN111651899A CN202010600476.4A CN202010600476A CN111651899A CN 111651899 A CN111651899 A CN 111651899A CN 202010600476 A CN202010600476 A CN 202010600476A CN 111651899 A CN111651899 A CN 111651899A
Authority
CN
China
Prior art keywords
station
battery
user selection
power
constraint
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010600476.4A
Other languages
Chinese (zh)
Inventor
张玉利
郭悦
张宁威
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN202010600476.4A priority Critical patent/CN111651899A/en
Publication of CN111651899A publication Critical patent/CN111651899A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G06Q30/0205—Location or geographical consideration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Data Mining & Analysis (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Finance (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • General Engineering & Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Quality & Reliability (AREA)
  • Probability & Statistics with Applications (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Algebra (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Computer Hardware Design (AREA)

Abstract

The invention discloses a robust site selection and capacity determination method and system for a power station in consideration of user selection behaviors, wherein the method comprises the following steps: establishing a user selection behavior model based on a plurality of Logit models, constructing a site selection model, and equivalently converting to form SOCP constraint; processing uncertain parameters by adopting a distributed robust optimization method, expressing the minimum number of batteries meeting the preset service level, and equivalently converting to form SOCP constraint; expressing the minimum number of the power conversion machines meeting the preset service level, and equivalently converting to form SOCP constraint; combining an SOCP constraint and an objective function of profit maximization, and constructing a robust siting constant volume model of the power station distribution based on the MISOCP model; and calling a solver to solve to obtain a site selection and volume fixing result of the power conversion station. According to the technical scheme, the user selection behavior and uncertainty are comprehensively considered, meanwhile, the profit maximization is realized, and the scientificity of site selection, station building and constant volume is improved.

Description

Robust site selection and volume determination method and system for power conversion station considering user selection behavior
Technical Field
The invention relates to the technical field of operation management and operation research, in particular to a robust locating and sizing method for a power changing station in consideration of user selection behaviors and a robust locating and sizing system for the power changing station in consideration of the user selection behaviors.
Background
With the global energy crisis and the increasing climate change, solutions are being sought in all countries of the world. In the field of transportation, electric vehicles are greatly supported for popularization. Currently, electric vehicle energy sources are supplemented with two modes: a charging mode and a battery replacement mode. However, the charging mode has the following disadvantages: long charging time, high investment cost and limited vehicle mileage. In order to solve the defects of the charging mode of the electric automobile, the electric automobile industry begins to explore the battery replacement mode. Although the battery change mode declared a failure in 2013 due to bankruptcy by Better Place corporation. In recent years, however, many businesses have begun to explore the battery swapping mode again. For example, the new energy of the northern gasoline is released to a "Optimus prime plan", aiming at deeply fusing a new energy automobile, a power battery, a battery replacement station and photovoltaic power generation, and planning to build 3000 light storage battery replacement stations and put 50 thousands of battery replacement vehicles in more than 100 cities across the country in 2022. In 2019, the strategic cooperation direction of 'longitudinal deepening fusion and transverse quick exploitation' is established at the group level by northern steam and Olympic, and the comprehensive popularization and application of the battery replacement mode are promoted together. In the next 5 years, the Olympic plan enters 50 cities in the country to build 5000 battery replacement stations, support 200 ten thousand new energy vehicles to replace electricity, and provide more efficient energy supply service for the cities.
The location selection and capacity fixing of the electric automobile battery replacement station are important for the battery replacement service operation. With respect to the addressing problem, the earliest emerged was a deterministic addressing model. Deterministic addressing models can be divided into three categories: a node-based addressing model, an arc-based addressing model, and a path-based addressing model. Currently, most addressing models are path-based models. Hodgson first started studying a path-based siting model that proposed a Flow trapping Location (FCL) model aimed at maximizing the total demand for service between a given pair of Origin-Destination (OD) points given the constraints of the number of stations built. The model assumes that a demand on a route can be serviced as long as there is a station on the route, but does not take into account the limited range of the vehicle. After Hodgson developed the FCL model, many scholars developed an improved model: an FRL (Flow cruising Location) model considering a vehicle cruising range, a CFRL (constrained Flow cruising Location) model considering a station capacity constraint, and the like. However, the above models all require a large number of feasible site selection point combinations to be generated in advance, so that the scholars improve the site selection point combinations to provide a new model: new node-based and arc-based FRL models, extend the network model. None of these models takes into account uncertainty in demand, user selection behavior, and service level requirements.
Disclosure of Invention
Aiming at the problems, the invention provides a robust siting and sizing method and a robust siting and sizing system for a power exchange station considering user selection behaviors, wherein a power exchange station distribution robust siting and sizing model is constructed under the condition of meeting a preset service level by establishing a user selection behavior model and a siting model and constructing a minimum number of batteries and a minimum number of power exchange machines required by a station under the condition of meeting a preset service level, and equivalently converted into a Mixed Integer Second Order Cone Programming (MISOCP) model, so that a siting and sizing result of the power exchange station of the electric automobile is obtained by solving. By the method provided by the invention, uncertainty and user selection behavior are comprehensively considered, meanwhile, profits of site selection and station building of operators, battery setting and battery changing robots can be maximized, and the scientificity of site selection and station building and capacity fixing is improved.
In order to achieve the purpose, the invention provides a robust locating and sizing method for a power station considering user selection behaviors, which comprises the following steps: aiming at the behavior of acquiring the battery swapping service by the user selection site, establishing a user selection behavior model based on a plurality of Logit models; constructing an addressing model based on the user selection behavior model; equivalently converting the site selection model to form a first Second-Order Cone Programming (SOCP) constraint; processing uncertain parameters by adopting a distributed robust optimization method, and expressing the minimum number of batteries required by the station under the condition of meeting the preset service level; equivalently converting the expression of the required minimum battery number to form a second SOCP constraint; expressing the minimum number of the electricity changing machines required by the station under the preset service level according to the user selection decision under the user selection behavior model; equivalently converting the expression of the minimum number of the battery replacement robots to form a third SOCP constraint; combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization, and constructing a power station distribution robust siting constant volume model based on the MISOCP model; and calling a solver to solve the power station distribution robust site selection constant volume model to obtain a site selection constant volume result of the electric automobile power station.
In the above technical solution, preferably, the expression of the minimum number of batteries required by a station satisfies a preset service level as follows: the probability that the State of Charge (SOC) of the battery is not lower than the preset value is not less than the preset probability; the expression of the minimum number of the power change machines required by the station satisfies the preset service level as follows: the average waiting time of the users does not exceed the preset time.
In the foregoing technical solution, preferably, the specific process of establishing the user selection behavior model based on multiple Logit models includes: under the condition that the site selection of the power swapping station is determined, the utility of the station to be selected is expressed through the power swapping distance and the crowding degree based on a plurality of Logit models; expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle; and obtaining the probability that a user selects a certain station to obtain the battery swapping service according to the battery swapping station set and the utility expression.
In the above technical solution, preferably, the specific process of processing the uncertain parameters by using the distributed robust optimization method and expressing the minimum number of batteries required by the station meeting the preset service level includes: based on a first-in first-out battery swap-out rule, expressing the probability that the SOC of a swapped-out battery is not lower than a preset value on the basis of the assumed battery number in a station, and enabling the probability not to be lower than the preset probability; and processing uncertain parameters in the expression by adopting a distributed robust optimization method, and converting to obtain a service level conditional expression which is required to be met by the minimum battery number under the user selection decision of the user selection behavior model.
In the above technical solution, preferably, in the process of expressing the minimum number of the power conversion robots required by the station under the preset service level: assuming that the battery swapping service process conforms to a GI/G/m queuing model, and obtaining the average waiting time of the user for acquiring the battery swapping service according to the number of battery swapping robots at the station and the mean and variance of the service rate of the battery swapping robots; and obtaining the expression of the minimum number of the battery replacement robots under the condition of the user selection decision given under the user selection behavior model, wherein the average waiting time is not more than the preset time.
The invention also provides a robust siting and sizing system for the power switching station in consideration of the user selection behavior, which is applied to the robust siting and sizing method for the power switching station in consideration of the user selection behavior in any one of the above technical schemes, and comprises the following steps: the user selection behavior modeling module is used for establishing a user selection behavior model based on a plurality of Logit models aiming at the behavior of obtaining the battery swapping service by the user selecting the site; the address selection modeling module is used for constructing an address selection model based on the user selection behavior model; the first constraint conversion module is used for equivalently converting the addressing model to form a first SOCP constraint; the battery number expression module is used for processing the uncertain parameters by adopting a distributed robust optimization method and expressing the minimum battery number required by the station under the condition of meeting the preset service level; the second constraint conversion module is used for equivalently converting the expression of the required minimum battery number to form a second SOCP constraint; the number of the power conversion robots is used for expressing the minimum number of the power conversion robots required by the station under the preset service level according to the user selection decision under the user selection behavior model; the third constraint conversion module is used for equivalently converting the expression of the minimum number of the battery swapping robots to form a third SOCP constraint; the locating and sizing modeling module is used for combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization to construct a power station distribution robust locating and sizing model based on the MISOCP model; and the solving module is used for calling a solver to solve the power station distribution robust site selection constant volume model to obtain a site selection constant volume result of the electric automobile power station.
In the foregoing technical solution, preferably, the preset service level to be satisfied by the expression constructed by the battery number expression module is: the probability that the SOC of the swapped-out battery is not lower than the preset value is not less than the preset probability; the preset service level to be met by the expression constructed by the number of the battery swapping machines is as follows: the average waiting time of the users does not exceed the preset time.
In the foregoing technical solution, preferably, the specific process of the user selection behavior modeling module establishing the user selection behavior model based on a plurality of Logit models includes: under the condition that the site selection of the power swapping station is determined, the utility of the station to be selected is expressed through the power swapping distance and the crowding degree based on a plurality of Logit models; expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle; and obtaining the probability that a user selects a certain station to obtain the battery swapping service according to the battery swapping station set and the utility expression.
In the above technical solution, preferably, the specific process of the battery number expression module processing the uncertain parameters by using a distributed robust optimization method and expressing the minimum number of batteries required by the station meeting the preset service level includes: based on a first-in first-out battery swap-out rule, expressing the probability that the SOC of a swapped-out battery is not lower than a preset value on the basis of the assumed battery number in a station, and enabling the probability not to be lower than the preset probability; and processing uncertain parameters in the expression by adopting a distributed robust optimization method, and converting to obtain the expression of the minimum battery number required by meeting a certain service level under the user selection decision of the user selection behavior model.
In the above technical solution, preferably, the process of the number of swapping machine people expression module expressing the minimum number of swapping machine people required by the station meeting the preset service level specifically includes: assuming that the battery swapping service process conforms to a GI/G/m queuing model, and obtaining the average waiting time of the user for acquiring the battery swapping service according to the number of battery swapping robots at the station and the mean and variance of the service rate of the battery swapping robots; and obtaining the expression of the minimum number of the battery replacement robots under the condition of the user selection decision given under the user selection behavior model, wherein the average waiting time is not more than the preset time.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of establishing a user selection behavior model and a site selection model, establishing a minimum battery number and a minimum number of battery changing machines needed by a station under the condition of meeting a preset service level, establishing a robust site selection constant volume model of a power changing station under the condition of maximizing the income, equivalently converting the robust site selection constant volume model into a MISOCP model, and solving to obtain a site selection constant volume result of the electric automobile power changing station. By the method provided by the invention, uncertainty and user selection behavior are comprehensively considered, meanwhile, profits of site selection and station building of operators, battery setting and battery changing robots can be maximized, and the scientificity of site selection and station building and constant volume is improved.
Drawings
Fig. 1 is a schematic flow chart of a robust siting and sizing method for a power conversion station in consideration of a user selection behavior according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a robust siting capacity system of a power swapping station in consideration of a user selection behavior according to an embodiment of the present invention.
In the drawings, the correspondence between each component and the reference numeral is:
11. the system comprises a behavior modeling module, a location modeling module, a first constraint conversion module, a battery number expression module, a second constraint conversion module, a battery swapping machine number expression module, a third constraint conversion module, a location and volume modeling module, a second constraint conversion module, a third constraint conversion module, a fourth constraint conversion module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the robust siting capacity method for a power conversion station considering a user selection behavior provided by the invention includes: aiming at the behavior of acquiring the battery swapping service by the user selection site, establishing a user selection behavior model based on a plurality of Logit models; constructing an address selection model based on the user selection behavior model; equivalently converting the addressing model to form a first SOCP constraint; processing uncertain parameters by adopting a distributed robust optimization method, and expressing the minimum number of batteries required by the station under the condition of meeting the preset service level; equivalently converting the expression of the required minimum battery number to form a second SOCP constraint; expressing the minimum number of the electricity changing machines required by the station under the preset service level according to a user selection decision under the user selection behavior model; equivalently converting the expression of the minimum number of the battery replacement robots to form a third SOCP constraint; combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization, and constructing a power station distribution robust siting constant volume model based on the MISOCP model; and calling a solver to solve the distributed robust site selection constant volume model of the power conversion station to obtain a site selection constant volume result of the electric automobile power conversion station.
In this embodiment, unlike the path-based power swapping requirement on the inter-city high-speed transportation network, the power swapping requirement inside a city is a node-based requirement. For example, electric taxi drivers inside cities often require supplemental energy after multiple trips. After the customer gets off the bus, the driver can select one of the sites which can provide the battery swapping service for the customer to obtain the battery swapping service according to personal preference based on the current position information, namely, user selection behaviors exist. In the embodiment, the user selection behavior, the uncertainty of the battery replacement requirement and the uncertainty of the SOC of the battery to be replaced are comprehensively considered, firstly, a plurality of Logit models are adopted to model the user selection behavior, uncertain parameters are processed by adopting a distributed robust optimization method, and a distribution robust location determination capacity model meeting a certain service level is constructed. Secondly, the model is equivalently converted into a MISOCP model.
Unlike private car energy replenishment, taxi drivers often do not replenish their energy until they have reached a designated location. The optional battery replacement stations are typically not unique to each driver when the customer disembarks. The driver selects a certain power exchanging station to exchange power with a certain probability by combining certain factors according to personal preference. From the perspective of an operator, under the condition that the battery replacement requirement and the SOC of a battery to be replaced are uncertain, some site selection nodes are selected from existing site selection nodes of the battery replacement station in a city to build the station, and a certain number of batteries and battery replacement robots are placed, so that the profit of the operator is maximized.
Different from the charging station, a certain number of batteries and charging robots need to be configured in the charging station. An excessive number of batteries may result in increased inventory holding costs, and an insufficient number may require the user to wait longer for a higher battery charge to be replaced. The investment cost is increased due to the fact that the number of the battery changing machines is too large, and the battery changing queuing waiting time of users is increased due to the fact that the number of the battery changing machines is too small. Therefore, the service level to be met by the site selection and capacity problem of the power station is defined from two aspects: for the minimum number of batteries required by the station, the SOC of the battery to be exchanged is not lower than a certain value with a certain probability, and for the minimum number of people who exchange the electric machines required by the station, the average waiting time of users is not more than a certain value.
And Q is a set of getting-off points of the electric taxi customers, and N is a set of battery replacement stations capable of serving the electric taxies. Define b separately1,b2,b3The purchase unit price of the battery, the purchase unit price of the battery replacement robot and the unit price of the battery replacement income. The purpose of the model is to determine the site selection and the constant volume of the power conversion station, so that the time-average profit of the power conversion station is maximized.
Three types of decision variables are defined:
yibuilding station at point i, and taking value as 1, i ∈ N, otherwise, taking value as 0
wiThe number of power change robots purchased at the point, i ∈ N
miPoint power change demand, i ∈ N
Aiming at the problems, the following model with the gain maximization as an objective function is established:
Figure BDA0002558389100000071
Figure BDA0002558389100000072
Figure BDA0002558389100000073
Figure BDA0002558389100000074
π∈Π(y),w∈W(y,π),m∈M(y,π) (5)
the method comprises the following steps that (1) is an objective function, the first term is the battery replacement income, the second term is the station building investment operation cost, the third term is the battery purchase cost, and the fourth term is the battery replacement robot purchase cost; constraints (2) and (3) are total cost for purchasing the battery and total cost for purchasing the battery replacement robot; constraints (4) - (5) define feasible domains of decision variables, wherein pi (y) is a feasible domain of the battery swapping amount of the battery swapping station and is determined by an address decision y, and W (y, pi) and M (y, pi) are feasible domains of the number of batteries purchased by the battery swapping station and are determined by the address decision y and the battery swapping demand pi.
In the above embodiment, preferably, each user may select whether to swap batteries or to which swap station to swap batteries according to the needs of the user and personal preferences under the condition that the location of the swap station is determined. The main factors influencing the power station selection behavior of the user have the following influences: a distance to the power change station, other service facilities near the power change station, and a number of service facilities at the power change station, and so on.
Based on a plurality of Logit models, the utility of the station to be selected is expressed through the electricity swapping distance and the crowding degree; expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle; and obtaining the probability of obtaining the power conversion service by selecting a certain station by the user according to the power conversion station set and the expression of the utility.
Specifically, the process of constructing the user-selected behavior model is as follows:
utility U for switching power from demand at q point to i pointqiFrom deterministic utility VqiAnd uncertainty utilityqiIs composed of, i.e.
Uqi＝Vqi+qi, (6)
Wherein the utility V is determinedqiThe method is determined by two parts: distance d for changing batteryqiAnd the degree of congestion at point i
Figure BDA0002558389100000085
Using the distance d from point q to point iqiThe average power change requirement of the predicted i point is used for representing the crowdedness degree of the point
Figure BDA0002558389100000086
Suppose VqiIs about dqiAnd
Figure BDA0002558389100000087
is a linear function of, and VqiWith respect to dqiAnd
Figure BDA0002558389100000088
are all negative correlations. Suppose that
Figure BDA0002558389100000081
Wherein, β0And β1Is dqiAnd
Figure BDA0002558389100000082
the weight coefficient of (2). Random utility in MNLqiAre independently and equally distributed.
After a site selection decision is given, a user can select a power conversion station according to the utility maximization principle. With NqA set of power change stations representing a demand that can serve a Q (Q ∈ Q) point, by a distance from the Q point not exceeding dq,maxOf points, i.e. Nq＝{i∈N:dqi≤dq,maxIn which d isq,maxIs the maximum swapping distance that the user at point q is willing to travel. In practical situations, the driver may choose not to change the battery for reasons such as distance or time, and we use i-0 to represent the situation that the user does not change the battery. According to the user selection behavior model, the probability P of the q-point user to swap battery at the swapping station iqiIs composed of
Figure BDA0002558389100000083
Simplifying it to obtain
Figure BDA0002558389100000084
Without loss of generality, V is still usedqiRepresents Vqi-Vq0Can obtain
Figure BDA0002558389100000091
And modeling the site selection problem of the power conversion station based on the user selection behavior model. The problem can be regarded as a two-stage problem, in the first stage, an operator determines the location of a power switching station, in the second stage, the location of the power switching station is determined, and a user selects the power switching station to obtain power switching service. Order to
Figure BDA0002558389100000092
Probability p of demand to power swap at point qqiIs composed of
Figure BDA0002558389100000093
When i is 0, i.e. the user does not change power, there is aq0＝1，y01. Thus, the probability p of demand to power swap at point qqiIs composed of
Figure BDA0002558389100000094
Next, the addressing model is converted to form an SOCP constraint:
order to
Figure BDA0002558389100000095
Thus can be used for
Figure BDA0002558389100000096
Is relaxed to
Figure BDA0002558389100000097
Figure BDA0002558389100000098
Equivalent to the following constraints:
Figure BDA0002558389100000101
thus p isqiIs defined by the following SOCP constraints:
Figure BDA0002558389100000102
in the above embodiment, preferably, based on a first-in first-out battery swap-out rule, the probability that the SOC of a swapped-out battery is not lower than a preset value is expressed on the basis of the assumed number of batteries in a station, and the probability is made not smaller than the preset probability; and processing uncertain parameters in the expression by adopting a distributed robust optimization method, converting to obtain the expression of the minimum number of batteries required by meeting a certain service level under the user selection decision of the user selection behavior model, and converting the expression into SOCP constraint.
Specifically, suppose that there are n batteries in one battery swapping station, the battery swapping request arrives randomly, let T be the time interval between two randomly arriving battery swapping requests, X be the SOC of the swapped-in battery, Z be the SOC of the swapped-out battery, and X and Z are both random variables.
Given a target value θ ∈ (0,1) for the swap-out battery SOC, the service level associated with the swap-out battery SOC is defined as Prob (Z ≧ θ). The battery swap-Out rule is assumed to be First In First Out (FIFO), namely, the battery which arrives First is charged First and swapped Out First, and In order to ensure that the probability that the SOC of the swapped-Out battery is not lower than theta is not lower than a certain value, Prob (Z is larger than or equal to theta) is ensured to be larger than or equal to 1 to belong to the element, wherein the element belongs to the element (0, 1).
In real life, the exact probability distribution functions of T and X are unknown, but their mean and variance information can be known from historical data. Therefore, the uncertain parameters are processed by a distributed robust optimization method based on moment information.
According to the following lemma: suppose that
Figure BDA0002558389100000106
Thus, for any one convex set S, there is
Figure BDA0002558389100000103
Wherein the content of the first and second substances,
Figure BDA0002558389100000104
suppose the expectation of the random variable Z is
Figure BDA0002558389100000105
Variance is Var [ Z ]]＝σ2Is greater than 0. Let S be { z: z < theta }. Because of the fact that
Figure BDA0002558389100000111
Therefore, it is not only easy to use
Figure BDA0002558389100000112
Three assumptions are made here:
1. the battery SOC increases by 1% per unit time of charge (if not fully charged).
2.
Figure BDA0002558389100000113
3.Ti、XiIs not relevant.
Let Y be (T)1,…,Tn,X1)T，μ＝(μT,…,μT,μX)TAnd Σ is the covariance matrix of Y. Based on the above assumptions, under the FIFO battery swapping strategy, the SOC of the swapped-out battery is
Figure BDA0002558389100000114
Consider the following:
Figure BDA0002558389100000115
is equivalent to
Figure BDA0002558389100000117
Due to the fact that
Figure BDA0002558389100000116
To ensure
Figure BDA00025583891000001211
Theta is required to be less than or equal to eTμθ≤eTμ，
Figure BDA0002558389100000121
Because of the fact that
Figure BDA0002558389100000122
Therefore, the minimum number of batteries required under the FIFO power conversion strategy should satisfy:
Figure BDA0002558389100000123
suppose θ ≧ μX. Order to
Figure BDA0002558389100000124
According to
Figure BDA0002558389100000125
Can obtain
Figure BDA0002558389100000126
As can be seen,
Figure BDA0002558389100000127
therefore, the first constraint in equation (23) is redundant. Therefore, the minimum number of batteries required under the FIFO strategy is given by equation (24).
Let fqFor the random power switching requirement at the requirement point Q ∈ Q, order
Figure BDA0002558389100000128
Is fqAnd assuming { f }qQ ∈ Q } are independent of each other, therefore, the swapping requirement at the swapping station i is
Figure BDA0002558389100000129
Order to
Figure BDA00025583891000001210
From the nature of the update process, for a given decision p, the mean and covariance of the random time interval between two electric vehicles arriving consecutively at node i can be known as
Figure BDA0002558389100000131
Under the FIFO strategy, the minimum number of batteries required at node i is as follows equation (25)
Figure BDA0002558389100000132
Next, the SOCP constraint is formed by translating the expression:
it is clear that,
Figure BDA0002558389100000133
is about
Figure BDA0002558389100000134
Is a monotonically increasing function of. Using decision variables wiIndicating the number of cells required at point i.
Order to
Figure BDA0002558389100000135
Then
Figure BDA0002558389100000136
Due to wi≥h(πi,τi,vi) With respect to tauiAnd viAnd (5) increasing the number of the single cells. Can be combined with
Figure BDA0002558389100000137
Is relaxed to
Figure BDA0002558389100000138
Figure BDA0002558389100000139
Equivalent to the following constraints:
Figure BDA00025583891000001310
finally, by introducing the variable uiCan obtain
Figure BDA0002558389100000141
Thus wiIs defined by the following SOCP constraints:
Figure BDA0002558389100000142
in the above embodiment, preferably, assuming that the battery swapping service process conforms to the GI/G/m queuing model, the average waiting time for the user to obtain the battery swapping service is obtained according to the number of battery swapping robots at a site and the mean and variance of the service rate of the battery swapping robots; and the average waiting time is not more than the preset time, and the expression of the minimum number of the battery replacement robots is obtained under the condition of the user selection decision given to the user selection behavior model and converted into SOCP constraint.
Specifically, assuming that the battery replacement station has m battery replacement robots, the mean and variance of the service rate of a single battery replacement robot are μs，
Figure BDA0002558389100000143
Mean and variance of arrival rate are assumed to be μr，
Figure BDA0002558389100000144
Let cs＝σs/μs，cT＝σT/μTWherein, muT＝1/μr，
Figure BDA0002558389100000145
The power conversion process of each station can be simulated as a GI/G/m queuing model, namely general arrival, general service and a plurality of service stations. According to the Allen-Cunneen approximation principle, the average waiting time of the user is
Figure BDA0002558389100000146
Where ρ ═ μr/(mμs)。
To ensure that the average waiting time of the user does not exceed L, the user must be forced to do so
Figure BDA0002558389100000151
Namely, it is
Figure BDA0002558389100000152
Wherein the content of the first and second substances,
Figure BDA0002558389100000153
thus, can obtain
Figure BDA0002558389100000154
Next, the SOCP constraint is formed by translating the expression:
for each point i, given a user selection decision p, there
Figure BDA0002558389100000155
Figure BDA0002558389100000156
The above constraints are equivalent to
Figure BDA0002558389100000157
Wherein the content of the first and second substances,
Figure BDA0002558389100000158
using the notation in (31), the above constraint can be written as
Figure BDA0002558389100000159
According to the three SOCP constraints, the objective function of profit maximization and the feasible regions of all decision variables, a power station distribution robust addressing volume model based on the MISOCP model is constructed and formed as follows:
Figure BDA0002558389100000161
Figure BDA0002558389100000162
Figure BDA0002558389100000163
Figure BDA0002558389100000164
Figure BDA0002558389100000165
Figure BDA0002558389100000166
Figure BDA0002558389100000167
Figure BDA0002558389100000168
Figure BDA0002558389100000169
Figure BDA00025583891000001610
Figure BDA00025583891000001611
Figure BDA00025583891000001612
Figure BDA00025583891000001613
Figure BDA00025583891000001614
Figure BDA00025583891000001615
Figure BDA00025583891000001616
and calling a solver to directly solve the distributed robust site selection constant volume model of the power station to obtain a site selection constant volume result of the electric vehicle power station.
The invention also provides a robust siting and sizing system for a power switching station in consideration of the user selection behavior, which is applied to the robust siting and sizing method for the power switching station in consideration of the user selection behavior, and comprises the following steps:
the user selection behavior modeling module 11 is used for establishing a user selection behavior model based on a plurality of Logit models aiming at the behavior of obtaining the battery swapping service by the user selecting a site;
the addressing modeling module 12 is used for constructing an addressing model based on a user selection behavior model;
a first constraint conversion module 13, configured to equivalently convert the addressing model to form a first SOCP constraint;
the battery number expression module 14 is used for processing the uncertain parameters by adopting a distributed robust optimization method and expressing the minimum battery number required by the station meeting the preset service level;
a second constraint conversion module 15, configured to equivalently convert the expression of the required minimum number of batteries to form a second SOCP constraint;
the number of the power conversion robots expression module 16 is used for expressing the minimum number of the power conversion robots required by the station under the preset service level according to the user selection decision under the user selection behavior model;
the third constraint conversion module 17 is configured to equivalently convert the expression of the minimum number of the battery swapping robots to form a third SOCP constraint;
the locating and sizing modeling module 18 is used for constructing a power station distribution robust locating and sizing model based on the MISOCP model by combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization;
and the solving module 19 is used for calling a solver to solve the power conversion station distribution robust site selection constant volume model to obtain a site selection constant volume result of the electric automobile power conversion station.
In the above embodiment, preferably, the preset service level to be satisfied by the expression constructed by the battery number expression module 14 is: the probability that the SOC of the swapped-out battery is not lower than the preset value is not less than the preset probability; the preset service level to be met by the expression constructed by the number of the battery swapping machines expression module 16 is as follows: the average waiting time of the users does not exceed the preset time.
In the foregoing embodiment, preferably, the specific process of the user selection behavior modeling module 11 building the user selection behavior model based on the multiple Logit models includes: under the condition that the site selection of the power swapping station is determined, the utility of the station to be selected is expressed through the power swapping distance and the crowding degree based on a plurality of Logit models; expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle; and obtaining the probability of obtaining the power conversion service by selecting a certain station by the user according to the power conversion station set and the expression of the utility.
In the foregoing embodiment, preferably, the specific process of the battery number expression module 14 processing the uncertain parameters by using a distributed robust optimization method and expressing the minimum number of batteries required by the station meeting the preset service level includes: based on a first-in first-out battery swap-out rule, expressing the probability that the SOC of the swapped-out battery is not lower than a preset value on the basis of the assumed battery number in a station, and enabling the probability to be not lower than the preset probability; and processing uncertain parameters in the expression by adopting a distributed robust optimization method, and converting to obtain the expression of the minimum number of batteries required by meeting a certain service level under the user selection decision of the user selection behavior model.
In the above embodiment, preferably, the process of the number of swapping machine persons expression module 16 expressing the minimum number of swapping machine persons required by the station under the preset service level specifically includes: assuming that the battery swapping service process conforms to a GI/G/m queuing model, and obtaining the average waiting time of the user for acquiring the battery swapping service according to the number of battery swapping robots at the station and the mean and variance of the service rate of the battery swapping robots; and the average waiting time is not more than the preset time, and the expression of the minimum number of the battery replacement robots is obtained under the condition of the user selection decision given to the user selection behavior model.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A robust site selection and volume fixing method for a power station considering user selection behaviors is characterized by comprising the following steps:
aiming at the behavior of acquiring the battery swapping service by the user selection site, establishing a user selection behavior model based on a plurality of Logit models;
constructing an addressing model based on the user selection behavior model;
equivalently converting the addressing model to form a first SOCP constraint;
processing uncertain parameters by adopting a distributed robust optimization method, and expressing the minimum number of batteries required by the station under the condition of meeting the preset service level;
equivalently converting the expression of the required minimum battery number to form a second SOCP constraint;
expressing the minimum number of the electricity changing machines required by the station under the preset service level according to the user selection decision under the user selection behavior model;
equivalently converting the expression of the minimum number of the battery replacement robots to form a third SOCP constraint;
combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization, and constructing a power station distribution robust siting constant volume model based on a mixed integer second-order cone programming model;
and calling a solver to solve the power station distribution robust site selection constant volume model to obtain a site selection constant volume result of the electric automobile power station.
2. The robust site selection sizing method considering the user selection behavior for the power change station as claimed in claim 1, wherein the expression of the minimum number of batteries required by the station satisfies a preset service level of: the probability that the state of charge of the battery to be exchanged is not lower than the preset value is not lower than the preset probability;
the expression of the minimum number of the power change machines required by the station satisfies the preset service level as follows: the average waiting time of the users does not exceed the preset time.
3. The robust siting capacity method for a power conversion station considering user selection behavior as claimed in claim 1, wherein the specific process of establishing the user selection behavior model based on the multiple Logit models comprises:
under the condition that the site selection of the power swapping station is determined, the utility of the station to be selected is expressed through the power swapping distance and the crowding degree based on a plurality of Logit models;
expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle;
and obtaining the probability that a user selects a certain station to obtain the battery swapping service according to the battery swapping station set and the utility expression.
4. The robust power station location determination method considering the user selection behavior as claimed in claim 2, wherein the specific process of processing the uncertain parameters by using the distributed robust optimization method and expressing the minimum number of batteries required by the station satisfying the preset service level comprises:
based on a first-in first-out battery swap-out rule, expressing the probability that the state of charge of a swapped-out battery is not lower than a preset value on the basis of the assumed battery number in a station, and enabling the probability not to be lower than the preset probability;
and processing uncertain parameters in the expression by adopting a distributed robust optimization method, and converting to obtain the expression of the minimum battery number required by meeting a certain service level under the user selection decision of the user selection behavior model.
5. The robust siting and sizing method for a power swapping station considering user selection behavior as claimed in claim 2, wherein in the process of expressing the minimum number of power swapping machines required by the station under a preset service level:
assuming that the battery swapping service process conforms to a GI/G/m queuing model, and obtaining the average waiting time of the user for acquiring the battery swapping service according to the number of battery swapping robots at the station and the mean and variance of the service rate of the battery swapping robots;
and obtaining the expression of the minimum number of the battery replacement robots under the condition of the user selection decision given under the user selection behavior model, wherein the average waiting time is not more than the preset time.
6. A robust siting system for a power switching station considering user selection behaviors, which applies the robust siting method for the power switching station considering user selection behaviors as claimed in any one of claims 1 to 5, and is characterized by comprising the following steps:
the user selection behavior modeling module is used for establishing a user selection behavior model based on a plurality of Logit models aiming at the behavior of obtaining the battery swapping service by the user selecting the site;
the address selection modeling module is used for constructing an address selection model based on the user selection behavior model;
the first constraint conversion module is used for equivalently converting the addressing model to form a first SOCP constraint;
the battery number expression module is used for processing the uncertain parameters by adopting a distributed robust optimization method and expressing the minimum battery number required by the station under the condition of meeting the preset service level;
the second constraint conversion module is used for equivalently converting the expression of the required minimum battery number to form a second SOCP constraint;
the number of the power conversion robots is used for expressing the minimum number of the power conversion robots required by the station under the preset service level according to the user selection decision under the user selection behavior model;
the third constraint conversion module is used for equivalently converting the expression of the minimum number of the battery swapping robots to form a third SOCP constraint;
the locating and sizing modeling module is used for constructing a power station distribution robust locating and sizing model on the basis of a mixed integer second-order cone programming model by combining the first SOCP constraint, the second SOCP constraint, the third SOCP constraint and an objective function of profit maximization;
and the solving module is used for calling a solver to solve the power station distribution robust site selection constant volume model to obtain a site selection constant volume result of the electric automobile power station.
7. The robust siting capacity system for a power conversion station considering user selection behavior as claimed in claim 6, wherein the preset service level to be satisfied by the expression constructed by the battery count expression module is: the probability that the state of charge of the battery to be exchanged is not lower than the preset value is not lower than the preset probability;
the preset service level to be met by the expression constructed by the number of the battery swapping machines is as follows: the average waiting time of the users does not exceed the preset time.
8. The robust siting capacity system for a power conversion station considering user selection behavior as claimed in claim 6, wherein the specific process of the user selection behavior modeling module establishing the user selection behavior model based on the plurality of Logit models comprises:
under the condition that the site selection of the power swapping station is determined, the utility of the station to be selected is expressed through the power swapping distance and the crowding degree based on a plurality of Logit models;
expressing a power change station set within the maximum power change distance which a user is willing to travel according to a utility maximization principle;
and obtaining the probability that a user selects a certain station to obtain the battery swapping service according to the battery swapping station set and the utility expression.
9. The robust power station location determination system considering the user selection behavior as claimed in claim 7, wherein the specific process of the battery number expression module processing the uncertain parameters by using a distributed robust optimization method and expressing the minimum number of batteries required by the station satisfying the preset service level comprises:
based on a first-in first-out battery swap-out rule, expressing the probability that the state of charge of a swapped-out battery is not lower than a preset value on the basis of the assumed battery number in a station, and enabling the probability not to be lower than the preset probability;
and processing uncertain parameters in the expression by adopting a distributed robust optimization method, and converting to obtain a service level conditional expression which is required to be met by the minimum battery number under the user selection decision of the user selection behavior model.
10. The robust siting capacity system for a power swapping station considering user selection behavior as claimed in claim 7, wherein the process of the number of power swapping robot expression module expressing the minimum number of power swapping robot required by the station meeting the preset service level specifically comprises:
assuming that the battery swapping service process conforms to a GI/G/m queuing model, and obtaining the average waiting time of the user for acquiring the battery swapping service according to the number of battery swapping robots at the station and the mean and variance of the service rate of the battery swapping robots;
and obtaining the expression of the minimum number of the battery replacement robots under the condition of the user selection decision given under the user selection behavior model, wherein the average waiting time is not more than the preset time.
CN202010600476.4A 2020-06-28 2020-06-28 Robust site selection and volume determination method and system for power conversion station considering user selection behavior Pending CN111651899A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010600476.4A CN111651899A (en) 2020-06-28 2020-06-28 Robust site selection and volume determination method and system for power conversion station considering user selection behavior

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010600476.4A CN111651899A (en) 2020-06-28 2020-06-28 Robust site selection and volume determination method and system for power conversion station considering user selection behavior

Publications (1)

Publication Number Publication Date
CN111651899A true CN111651899A (en) 2020-09-11

Family

ID=72347611

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010600476.4A Pending CN111651899A (en) 2020-06-28 2020-06-28 Robust site selection and volume determination method and system for power conversion station considering user selection behavior

Country Status (1)

Country Link
CN (1) CN111651899A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112581313A (en) * 2020-12-23 2021-03-30 北京理工大学 Photovoltaic charging station resource distribution and adjustment method and system
CN115438840A (en) * 2022-08-15 2022-12-06 北京化工大学 Site selection optimization method for electric vehicle power changing station with controllable average waiting time

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103241130A (en) * 2013-04-10 2013-08-14 华中科技大学 Energy management method and system for electric bus charging and swap station
US20180240047A1 (en) * 2015-08-19 2018-08-23 Tianjin University Planning method of electric vehicle fast charging stations on the expressway
CN109492794A (en) * 2018-10-08 2019-03-19 国网天津市电力公司电力科学研究院 A kind of electric automobile charging station allocation optimum Mathematical Modeling Methods based on two step equivalence methods

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103241130A (en) * 2013-04-10 2013-08-14 华中科技大学 Energy management method and system for electric bus charging and swap station
US20180240047A1 (en) * 2015-08-19 2018-08-23 Tianjin University Planning method of electric vehicle fast charging stations on the expressway
CN109492794A (en) * 2018-10-08 2019-03-19 国网天津市电力公司电力科学研究院 A kind of electric automobile charging station allocation optimum Mathematical Modeling Methods based on two step equivalence methods

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
刘敏等: "一种电动汽车换电站的布局定容方法", 《电气自动化》 *
杨现青等: "电动汽车充电站选址优化研究", 《交通科技与经济》 *
邵赛等: "考虑排队时间和里程约束的竞争充电站选址问题", 《交通运输***工程与信息》 *
钱斌等: "电动公交车换电站―电池充电站优化规划", 《电力***自动化》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112581313A (en) * 2020-12-23 2021-03-30 北京理工大学 Photovoltaic charging station resource distribution and adjustment method and system
CN112581313B (en) * 2020-12-23 2022-02-22 北京理工大学 Photovoltaic charging station resource distribution and adjustment method and system
CN115438840A (en) * 2022-08-15 2022-12-06 北京化工大学 Site selection optimization method for electric vehicle power changing station with controllable average waiting time

Similar Documents

Publication Publication Date Title
Hussain et al. Efficient power management algorithm based on fuzzy logic inference for electric vehicles parking lot
Li et al. Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm
Li et al. Price incentive-based charging navigation strategy for electric vehicles
CN111915176B (en) Scheduling method and system for pure electric bus in hybrid operation mode
Xiang et al. Electric vehicles in smart grid: a survey on charging load modelling
Zhu et al. Charging station planning for plug-in electric vehicles
Tucker et al. Online charge scheduling for electric vehicles in autonomous mobility on demand fleets
Ji et al. Optimal electric bus fleet scheduling for a route with charging facility sharing
Guo et al. Vehicle rebalancing with charging scheduling in one-way car-sharing systems
CN110796286B (en) Flexible planning method of power distribution system suitable for electric automobile large-scale application
Sadhukhan et al. Optimal allocation of EV charging stations in a radial distribution network using probabilistic load modeling
Qureshi et al. Scheduling and routing of mobile charging stations with stochastic travel times to service heterogeneous spatiotemporal electric vehicle charging requests with time windows
CN111651899A (en) Robust site selection and volume determination method and system for power conversion station considering user selection behavior
Zeng et al. Optimization of future charging infrastructure for commercial electric vehicles using a multi-objective genetic algorithm and real travel data
Zhang et al. Stochastic modeling and analysis of public electric vehicle fleet charging station operations
Ma Two-stage battery recharge scheduling and vehicle-charger assignment policy for dynamic electric dial-a-ride services
Shen et al. Integrated optimization of electric vehicles charging location and allocation for valet charging service
CN112907153B (en) Electric vehicle scheduling method considering various requirements of user in mixed scene
CN111582581A (en) Electric automobile battery replacement station robust site selection method considering random traffic flow offset
Fan et al. Optimal shared mobility planning for electric vehicles in the distribution network
CN109978241A (en) A kind of determination method and device of electric car charging load
CN106682759B (en) Battery supply system for electric taxi and network optimization method
CN112613682A (en) Electric vehicle charging load prediction method
Ruiz et al. An optimal battery charging and schedule control strategy for electric bus rapid transit
Zhang et al. Optimizing the strategic decisions for one-way station-based carsharing systems: A mean-CVaR approach

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200911