CN115545486A - Electric vehicle charging scheduling method based on block chain - Google Patents
Electric vehicle charging scheduling method based on block chain Download PDFInfo
- Publication number
- CN115545486A CN115545486A CN202211237885.8A CN202211237885A CN115545486A CN 115545486 A CN115545486 A CN 115545486A CN 202211237885 A CN202211237885 A CN 202211237885A CN 115545486 A CN115545486 A CN 115545486A
- Authority
- CN
- China
- Prior art keywords
- user
- scheduling
- charging
- block chain
- charging station
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 33
- 239000002245 particle Substances 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 11
- 230000006399 behavior Effects 0.000 claims description 18
- 230000005611 electricity Effects 0.000 claims description 16
- 230000006870 function Effects 0.000 claims description 8
- 230000000694 effects Effects 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 230000008901 benefit Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 claims description 2
- 238000005265 energy consumption Methods 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 claims description 2
- 238000000638 solvent extraction Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 7
- 238000005457 optimization Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- 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
- G06Q30/0283—Price estimation or determination
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- General Health & Medical Sciences (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Accounting & Taxation (AREA)
- Primary Health Care (AREA)
- Biophysics (AREA)
- Public Health (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Water Supply & Treatment (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
Abstract
The invention provides an electric vehicle charging scheduling method based on a block chain. The invention designs a safety scheduling architecture based on a block chain, realizes information intercommunication during electric vehicle scheduling by using the block chain technology, and ensures user transaction and privacy safety. Entities participating in the scheduling process and the scheduling flow are specified in the safe scheduling architecture. In addition to the charging station price, the invention designs an overhead calculation method based on user behavior, and the charging station is selected for the user by integrating the user behavior. The user behavior comprises factors such as user reputation, overhead generated in the user charging process, user prediction and the like. The improved particle swarm algorithm is utilized to realize the scheduling aiming at reducing the user overhead and reducing the peak-valley difference of the power grid, the scheduling algorithm is realized by utilizing the intelligent contract technology and is operated on a block chain, and the charging scheduling can be efficiently, safely and automatically realized. The invention can meet the aims of reducing the peak-valley difference of the power grid and reducing the cost of users, realizes safe and reasonable charging scheduling and reduces the load pressure of the power grid.
Description
Technical Field
The invention belongs to the field of electric automobile charging.
Background
Since the industrial revolution, the climate problems caused by the emission of large amounts of greenhouse gases have become more severe. Electric Vehicles (EVs) have great potential in relieving the fossil fuel crisis and reducing gas emissions, and thus are receiving widespread attention from various countries. With the rapid increase of the number of EVs, the problem caused by the disordered charging of a large number of electric vehicles is increasingly highlighted. The EV charging load is easy to cause the peak-to-peak phenomenon of the power grid, and challenges are brought to the stability of the power grid, so that other social activities are influenced. Meanwhile, messages between the user and the charging station are not intercommunicated, so that the user is difficult to select the optimal charging station, the utilization rate difference of different charging facilities is large, and the like. Blockchains are a new distributed computing and storage paradigm that merges multiple existing technologies. The distributed consensus algorithm is used for generating and updating data, peer-to-peer network is used for data transmission among nodes, a distributed account book combining technologies such as a cryptology principle and a timestamp guarantees that stored data cannot be tampered, and an upper application logic is realized by using an automatic script code or an intelligent contract. The method has the characteristics of credibility and decentralization of the process, and can build a trust foundation in a low-cost mode under the scene of participation of multiple beneficial agents. Therefore, the block chain technology is applied to electric vehicle charging scheduling, the electric vehicle charging scheduling can be realized by using an intelligent contract on the premise of ensuring user transaction and privacy safety, the peak-trough difference of a power grid can be reduced by reasonable scheduling logic, the user overhead is reduced, and the problems are solved.
On the aspect of electric vehicle charging scheduling, a particle swarm algorithm-based electric vehicle charging scheduling optimization method is provided, and the method comprises the following steps: 1) When the electric vehicle is in a low electric quantity value, a user sends a charging request to a server, and after receiving the request, the server collects the residual energy of the battery of the electric vehicle, the air-conditioning state, the current position of the electric vehicle and the distribution situation of peripheral charging stations, and meanwhile, the congestion situation of surrounding roads needs to be referred to; 2) Gridding the road, and describing the electric vehicle charging scheduling problem as an optimization problem; 3) Aiming at the problem model, selecting a proper target charging station and an optimal running path to the target charging station for the electric vehicle by adopting a particle swarm algorithm; 4) And as long as the server and the electric vehicle are in a connection state, the server sends the information of the target charging station and the optimal path to the target charging station to the user.
The existing electric vehicle charging scheduling method has the following defects:
(1) On the grid side, production activities in modern society mostly depend on power sources. The disordered charging of a large number of electric vehicles easily causes 'peak-to-peak' of a power grid, and challenges are brought to the stability of the power grid, so that other social production activities are influenced. Therefore, how to plan the orderly charging of the electric automobile to achieve the purpose of 'peak clipping and valley filling' needs to be researched.
(2) On the user side, although the number of charging stations is limited, the owner of the vehicle in a single region can select charging stations at different positions according to actual conditions. How to quickly select the charging station with the least cost in consideration of the conditions of time, money, mileage, etc. has certain difficulty. Therefore, it is also an indispensable point in the charging problem to comprehensively consider various conditions and help the user to make a selection.
(3) In the aspect of security, the existing schemes mostly rely on a third-party server to implement scheduling, and once a third party is not trusted or attacked, the privacy of a user faces the risk of disclosure, which is unfavorable to the security of the user. Therefore, the safety problem is also a key problem in the electric vehicle charging schedule.
Disclosure of Invention
The invention designs an electric vehicle charging scheduling method based on a block chain aiming at the problems. The main characteristics are as follows:
(1) The introduction of a third party is avoided, a safety scheduling architecture based on a block chain is designed, information intercommunication during electric vehicle scheduling is realized by using the block chain technology, and the transaction and privacy safety of users is ensured. Entities participating in the scheduling process and the scheduling flow are specified in the safe scheduling architecture.
(2) In addition to the charging station price, the invention designs a user behavior-based cost calculation method for reducing the user cost, and integrates the user behavior to select the charging station for the user. The user behavior comprises factors such as user reputation, overhead generated in the user charging process, user prediction and the like.
(3) The improved particle swarm algorithm is utilized to realize scheduling aiming at reducing user overhead and reducing power grid peak-valley difference, and finally, the scheduling algorithm is realized by utilizing an intelligent contract technology and runs on a block chain, so that charging scheduling can be efficiently and automatically realized, and the safety of the scheduling process is enhanced.
In conclusion, the invention can meet the aims of reducing the peak-valley difference of the power grid and reducing the cost of users, realizes safe and reasonable electric vehicle charging scheduling, provides convenient service for the users and reduces the load pressure of the power grid.
The invention describes the electric vehicle charging scheduling method based on the block chain in detail from the general architecture to the specific flow. Firstly, the invention provides a safety scheduling framework based on a block chain, and analyzes the specific action of each object in scheduling and the whole scheduling flow; then, an overhead calculation method based on user behaviors is provided, and personalized pricing of the charging station and cost calculation of the user are achieved; and finally, improving the particle swarm algorithm and realizing an intelligent contract, so as to achieve the purposes of reducing the peak pressure of a power grid and minimizing the user overhead, and finally realize the charging scheduling of the electric automobile.
1. The invention architecture and functional module partitioning
The system architecture of the present invention is shown in fig. 1. The electric vehicle charging needs to involve three entities, namely a charging station, a block chain and a user.
(1) The user: the system consists of electric automobiles which are dispatched. When the electric automobile needs to be charged, basic information (such as automobile position, required electric quantity, current electric quantity, credit score, power consumption per kilometer at normal driving speed and the like) of the current automobile needs to be uploaded to the block chain. And after the scheduling result is obtained, the vehicle obtains the updated credit score according to whether the user performs according to the scheduling result or not so as to be used for the next scheduling.
(2) Charging station: and is responsible for providing power resources for users. Before starting scheduling, the charging station needs to upload basic information (including location, time-of-use price and the like) to the blockchain, and provides service when the user is charged in the future.
(3) Block chains: and the system is responsible for recording basic information and transaction information, finishing scheduling decision and information issuing and updating user credit scores. After receiving the relevant information of the charging station and the user, the block chain carries out charging scheduling on the vehicle by utilizing the intelligent contract on the block chain. And then updating the credit of the electric vehicle according to the performance condition.
The whole transaction flow of the electric vehicle charging scheduling is as follows: 1. first, the charging station uploads basic information (including contents such as position and time-of-use electricity price) to the block chain, so that subsequent calculation and use are facilitated. 2. The electric vehicle then makes a charging demand and uploads information (vehicle location, amount of power needed, current amount of power, reputation score, etc.) to the blockchain. 3. And (4) comprehensively considering multiple factors by using a scheduling algorithm on the intelligent contract, carrying out peak clipping and valley filling to select the most appropriate charging station for the user, and charging the electric vehicle to the designated charging station according to the scheduling condition. 5. Finally, the vehicle credit is updated according to the vehicle performance condition, and the charging price can be conveniently adjusted according to the credit next time.
A plurality of charging stations are available in the same area and are responsible for providing electric power for electric vehicles for transaction. The block chain is managed jointly by the plurality of charging stations, so that the centralized situation caused by the management of a single charging station is avoided, namely all transactions are supervised jointly by the plurality of charging stations, and the fairness and traceability are realized. The invention can be divided into three functional modules, namely a safety scheduling framework based on a block chain, an overhead calculation method based on user behaviors, a scheduling strategy based on an improved particle swarm algorithm and the realization of an intelligent contract. The overhead calculation method is used as a part of a scheduling target in the scheduling strategy. The relationship of the three parts is shown in fig. 2. The flow of the three sections will be described in detail below.
2. Safety scheduling architecture based on block chain
The safety scheduling architecture can be mainly divided into four stages, namely identity verification, information uploading, contract execution and user charging.
(1) Authentication phase-during this phase, the user and the charging station need to perform a series of operations locally to initialize some system parameters. Firstly, a public key (Plk) and a private key (Prk) are generated according to a system-agreed encryption algorithm (RSA encryption algorithm, elliptic curve algorithm, etc.). The verification information is verified through the block chain, and the next stage can be continued after the verification is passed.
(2) In the information uploading stage, the user and the charging station upload the basic information to the block chain
(3) In the contract execution stage, two sections of contracts need to be executed, and after a user puts forward a charging requirement, decision is made according to an intelligent contract converted by a scheduling algorithm to realize scheduling; and after the user obtains the scheduling result to charge, executing the intelligent contract converted by the credit updating function.
(4) And (3) a user charging stage: and the user carries out charging according to the scheduling result.
3. Overhead calculation method based on user behavior
In the charging process of the electric automobile, in order to help a user select a charging station with the minimum expenditure, the subject designs an expenditure calculation method based on user behaviors. The user behavior needs to consider the consumption of the user on the way to the charging station, the user, etc., the user charging period, the user reputation, etc. When the user performs according to the scheduling, the reputation score will rise, otherwise, the reputation score will drop, and the user reputation is directly related to the selling price of the charging station electricity. The cost of the user to different charging stations is different according to different user behaviors. The cost calculation method based on the user behaviors is more reasonable than a selection method only considering the electricity selling price of the charging station, can comprehensively consider various factors for the user at the same time, and avoids extreme conditions that the electricity selling price is low, but the distance of the charging station is far or the time is long, and the like. The flow chart of this module is shown in fig. 4. The method is mainly divided into two stages of charging station pricing and user expense calculation.
(1) Pricing a charging station: due to the fact that the wave crest and trough difference and the economic benefit are reduced, charging station pricing changes along with time, and a time-sharing pricing mechanism is adopted. Secondly, in order to achieve the effect that pricing is influenced by reputation, reputation is also one of the pricing factors of the charging station. Different users have different reputations, charging stations at the same time are priced differently, and the higher the reputation of the user is, the lower the pricing is (the price is greater than the cost price), and otherwise, the higher the pricing is. Personalized pricing is achieved. And then calculating the electricity purchasing expense according to the required electricity quantity. Let charging station S E [ S [ ] 0 ，S 1 ，S 2 ···S N ]The charging station pricing formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the pricing of the charging station s in the time period t is determined by the time-of-use electricity price and the user credit part; in the present invention, a day is divided into 24 periods.
Is the time-of-use electricity price of the current charging station at time period t; k is a radical of s Is the price adjustment factor, k, of the charging station s The larger the magnitude of the adjustment.
Is the reputation score of the user in the schedule,
rep =1 represents that the user performs the contract on time last time, the pricing of the charging station at this time is reduced on the basis of the time-of-use electricity price, and is increased on the contrary; the pricing method not only ensures that the income proportion of the charging station is adjustable, but also achieves the purpose of personalized pricing for users through credit.
The price adjustment factor is formulated by each charging station, and the larger the price adjustment factor is, the more influenced the reputation of the user on the service pricing of the user by the charging station is.
(2) And (3) calculating user overhead: the user cost is based on user behaviors, the distance between the vehicle and each charging station is calculated firstly, and then the distance cost is calculated according to the energy consumption cost of each kilometer of the vehicle. Secondly, it is possible that the current charging station does not have an idle charging pile, needs to wait for the charging, and the cost of the vehicle waiting time is calculated according to waiting time. The electricity purchase cost, the journey cost and the isochronous cost jointly form the whole cost of each charging. The calculation formula of the user overhead is as follows: cost i ＝Pcost i +Dcost i
Among them, cost i Is the cost of the electric vehicle i, which depends on the charge cost Pcost i And go to fillDistance cost Dpost of a power station i 。Pcost i The calculation method of (2) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the charging price of the electric automobile depends on a calculation formula of charging station pricing; eta i Is the charge amount applied by the electric automobile. Cost Dpost consumed by electric automobile on charging circuit i The calculation formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the distance from the current electric vehicle to a charging station; mu.s i Is the average cost per kilometer of the current vehicle on the roadway.
4. Implementation of scheduling strategy and intelligent contract based on improved particle swarm optimization
The invention provides a scheduling strategy based on an improved particle swarm algorithm, and the charging scheduling of an electric automobile is completed. The scheduling target is to reduce the peak-to-trough difference and reduce the user overhead. The multi-objective optimization problem can be integrated into an objective function for processing. The multi-target particle swarm algorithm has the characteristics of easiness in implementation, high convergence speed and good effect, but the traditional multi-target particle swarm algorithm is easy to fall into local optimization. Considering that the scheduling algorithm needs to be operated on an intelligent contract, in order to reduce operation burden and accelerate convergence speed, the particle swarm algorithm needs to be improved, and finally the adjusted algorithm is realized on the intelligent contract to complete the scheduling of the automobile. The flow of the module is shown in FIG. 5.
Drawings
FIG. 1 System architecture diagram
FIG. 2 shows the relationship between the modules
FIG. 3 is a flow chart of a secure dispatch architecture
FIG. 4 overhead calculation flow chart
FIG. 5 scheduling policy enforcement flow chart
Detailed Description
Firstly, modeling is carried out, and a multi-target particle swarm algorithm is adopted for solving. The feasible solution is considered as a particle, assuming that there is no mass and volume of the particle, the population size is popsize. The scheduling strategy based on the particle swarm algorithm considers that one day is 24 hours, and the number of the electric vehicles is V. To facilitate subsequent matrix calculations, the present strategy extends the particles to a space of dimensions 24 × V, and the position of the particle p in the solution space is represented as a matrix of dimensions 24 × V. The particles are randomly distributed in the solution space as initialization of the particles. And gathering the iteration to a place with high target function fitness to obtain a scheduling result. And secondly, calculating the peak-trough difference of the power grid under the scheduling condition by using the current particle scheduling condition. And combining a user overhead calculation method to obtain the target function. The calculation formula of the objective function is as follows:
among them, fitness p Is the fitness of the particle p according to the current scheduling policy.
The charging expense of the V-quantity electric automobile is summed up; dif is the difference between the peaks and the troughs of the power grid; omega 1 And omega 2 Is the weight of each part, which is 0.5.
To achieve the scheduling goal, the goal of each iteration is Fitness p And (4) minimizing.
The global search parameter w of the traditional particle swarm algorithm is a fixed value and does not change along with the iteration process, and the method optimizes the global search parameter w so that the global search parameter w changes along with the iteration times. In the early stage of searching, the global searching range is expanded, and in the later stage, the searching range is reduced, so that the convergence speed is accelerated. And finally, converting the designed scheduling strategy into Go language, compiling into an intelligent contract, and issuing the intelligent contract on the block chain to realize the operation of the scheduling strategy on the block chain. The information collection and decision making process is guaranteed to be independent of a third party, and privacy and transaction safety are protected.
1. The invention provides a block chain-based electric vehicle charging scheduling method, which is divided and explained from a general architecture to specific modules. The method is characterized in that a whole-course safe electric automobile charging scheduling is provided based on a block chain, and the user overhead is reduced while the peak-valley difference of a power grid is reduced.
2. The invention provides a safety scheduling architecture based on a block chain, which is characterized in that the block chain is utilized to ensure the transaction and privacy safety of a user, and the whole scheduling process is transparent and traceable.
3. The invention provides a user behavior-based overhead calculation method which is characterized in that personalized pricing based on reputation can be realized, and overhead factors are considered comprehensively.
4. The invention provides a scheduling strategy based on an improved particle swarm algorithm and an implementation of an intelligent contract, which is characterized in that algorithm parameters are improved and applied to the scheduling problem of electric vehicles, and the purposes of reducing the peak-valley difference of a power grid and reducing the cost of users are achieved.
Claims (2)
1. An electric vehicle charging scheduling method based on a block chain is characterized by comprising the following steps:
1. architecture and functional module partitioning
Three entities are involved in the electric vehicle charging, namely a charging station, a block chain and a user;
(1) The user: the system consists of electric automobiles which are dispatched; when the electric automobile needs to be charged, basic information of the current automobile, including automobile position, required electric quantity, current electric quantity, credit score and power consumption per kilometer at normal driving speed, needs to be uploaded to the block chain; after the scheduling result is obtained, the vehicle obtains an updated credit score for the next scheduling according to whether the user performs according to the scheduling result;
(2) Charging station: the system is responsible for providing power resources for users; before the dispatching is started, a charging station needs to upload basic information to a block chain, wherein the basic information comprises positions and time-of-use electricity prices, and services are provided when a user charges the block chain in the future;
(3) Block chains: the system is responsible for recording basic information and transaction information, finishing scheduling decision and information issuing and updating user credit scores; after receiving the relevant information of the charging station and the user, the block chain utilizes the intelligent contract on the block chain to carry out charging scheduling on the vehicle; then updating the credit of the electric vehicle according to the performance condition;
the whole transaction flow of the electric vehicle charging scheduling is as follows: 1) Firstly, the charging station uploads basic information including position and time-of-use electricity price to the block chain; 2) Then the electric automobile puts forward a charging demand, and uploads information including automobile position, required electric quantity, current electric quantity and credit score to a block chain; 3) Selecting the most appropriate charging station for the user by the scheduling algorithm, 4) charging the electric vehicle to the designated charging station according to the scheduling condition; 5) Finally, the vehicle credit is updated according to the vehicle performance condition, so that the charging price can be conveniently adjusted according to the credit next time;
a plurality of charging stations are available in the same area and are responsible for providing electric power for the electric automobile for transaction; the block chain is managed by the charging stations together, so that the centralized situation caused by the overall management of a single charging station is avoided, namely all transactions are supervised by the charging stations together, and the transactions are fair and traceable; the method comprises the following steps of dividing the system into three functional modules, and realizing a safety scheduling architecture based on a block chain, an overhead calculation method based on user behaviors, a scheduling strategy based on an improved particle swarm algorithm and an intelligent contract; the overhead calculation method is used as a part of a scheduling target in a scheduling strategy; the flow of the three parts will be described in detail below;
2. block chain based security scheduling architecture
The safety scheduling architecture is divided into four stages, namely identity verification, information uploading, contract execution and user charging;
(1) An authentication phase, in which a user and a charging station need to perform a series of operations locally to initialize some system parameters; firstly, generating a public key and a private key according to an encryption algorithm agreed by a system; then, the verification information is verified through the block chain, and the next stage can be continued after the verification is passed;
(2) In the information uploading stage, the user and the charging station upload the basic information to the block chain
(3) In the contract execution stage, two sections of contracts need to be executed, and after a user puts forward a charging requirement, decision is made according to an intelligent contract converted by a scheduling algorithm to realize scheduling; after the user obtains the scheduling result for charging, executing an intelligent contract converted by the credit updating function;
(4) And (3) a user charging stage: charging a user;
overhead calculation method based on user behavior
In the charging process of the electric automobile, the consumption of a user in the way of the user to a charging station, the user and the like, the charging time period of the user and the credit of the user need to be considered in the user behavior; when the user performs according to the scheduling, the credit score will increase, otherwise, the credit score will decrease, and the user credit is directly related to the selling price of the charging station electricity;
according to different user behaviors, the expenses of the user to different charging stations are different; the overhead calculation method based on the user behavior comprises two stages of charging station pricing and user overhead calculation;
(1) Pricing a charging station: in consideration of reducing wave peak and trough difference and economic benefit, the pricing of the charging station changes along with time, and a time-sharing pricing mechanism is adopted; secondly, in order to achieve the effect that pricing is influenced by credit, credit is also one of the pricing factors of the charging station; different users have different reputations, charging stations at the same time have different pricing, and the higher the reputation of the user is, the lower the pricing is, otherwise, the higher the pricing is; then, calculating electricity purchasing cost according to the required electricity quantity;
(2) And (3) calculating user overhead: the user cost is based on user behaviors, the distance between the vehicle and each charging station is calculated firstly, and then the distance cost is calculated according to the energy consumption cost of the vehicle per kilometer; secondly, the current charging station is possible to have no idle charging pile and needs to wait for waiting, and the vehicle waiting expense is calculated according to waiting time; the electricity purchasing expense, the route expense and the isochronous expense jointly form the whole expense of each charging;
fourth, based on the implementation of the scheduling tactics and intellectual contracts of the particle swarm algorithm
A scheduling strategy based on a particle swarm algorithm is provided to complete the charging scheduling of the electric automobile; the scheduling target is to reduce the peak-to-trough difference and reduce the user overhead; finally, the adjusted algorithm is realized on the intelligent contract to finish the dispatching of the automobile.
2. The method according to claim 1, characterized in that step four is specifically as follows: firstly, modeling is carried out, and a multi-target particle swarm algorithm is adopted for solving; taking the feasible solution as a particle, searching in a solution space meeting constraint conditions, and randomly initializing the particle; gathering the iteration to a place with high target function fitness to obtain a scheduling result; secondly, calculating the peak-trough difference of the power grid under the current particle scheduling condition; obtaining a target function by combining a user overhead calculation method; optimizing the global search parameter w to enable the global search parameter w to change along with the iteration times; in the early stage of searching, the global searching range is expanded, and in the later stage, the searching range is reduced, so that the convergence speed is accelerated; and finally, converting the designed scheduling strategy into Go language, compiling into an intelligent contract, and issuing the intelligent contract on the block chain to realize the operation of the scheduling strategy on the block chain.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211237885.8A CN115545486A (en) | 2022-10-11 | 2022-10-11 | Electric vehicle charging scheduling method based on block chain |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211237885.8A CN115545486A (en) | 2022-10-11 | 2022-10-11 | Electric vehicle charging scheduling method based on block chain |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115545486A true CN115545486A (en) | 2022-12-30 |
Family
ID=84733573
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211237885.8A Pending CN115545486A (en) | 2022-10-11 | 2022-10-11 | Electric vehicle charging scheduling method based on block chain |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115545486A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090675A (en) * | 2023-04-10 | 2023-05-09 | 成都信息工程大学 | Short-time charging scheduling method based on combination of block chain and neural network |
-
2022
- 2022-10-11 CN CN202211237885.8A patent/CN115545486A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090675A (en) * | 2023-04-10 | 2023-05-09 | 成都信息工程大学 | Short-time charging scheduling method based on combination of block chain and neural network |
| CN116090675B (en) * | 2023-04-10 | 2023-06-09 | 成都信息工程大学 | Short-time charging scheduling method based on combination of block chain and neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109166036B (en) | V2G energy trading method based on block chain and contract theory | |
| Alvaro-Hermana et al. | Peer to peer energy trading with electric vehicles | |
| Luo et al. | Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems | |
| Wang et al. | Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method | |
| CN103797334B (en) | For providing the system and method for the route planning of plug-in hybrid vehicle | |
| Fluhr et al. | A stochastic model for simulating the availability of electric vehicles for services to the power grid | |
| Yang et al. | A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem | |
| Chekired et al. | Fog-computing-based energy storage in smart grid: A cut-off priority queuing model for plug-in electrified vehicle charging | |
| Guner et al. | Stochastic energy storage capacity model of EV parking lots | |
| Jin et al. | Energy efficient algorithms for electric vehicle charging with intermittent renewable energy sources | |
| Piazza et al. | Optimal design of electric mobility services for a Local Energy Community | |
| CN111563786A (en) | Virtual power plant regulation and control platform based on block chain and operation method | |
| CN111612358A (en) | Shared automobile vehicle scheduling and dispatcher path optimization method | |
| KR20200043084A (en) | System and method for managing carbon gases emission reductions based on amount of electric vehicle charge | |
| CN112668791A (en) | Optimization method of combined heat and power system | |
| Rajani et al. | A hybrid optimization based energy management between electric vehicle and electricity distribution system | |
| 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 | |
| CN115545486A (en) | Electric vehicle charging scheduling method based on block chain | |
| CN114169609A (en) | Photovoltaic coupling-considered electric vehicle charging station planning method | |
| Ejaz et al. | Internet of Things enabled electric vehicles in smart cities | |
| Wang et al. | Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet | |
| Shi et al. | Efficient energy management of wireless charging roads with energy storage for coupled transportation–power systems | |
| CN114936666A (en) | Electric vehicle charging navigation method and system based on vehicle-station-platform system | |
| Li et al. | Route optimization of electric vehicles based on reinsertion genetic algorithm | |
| CN115511172A (en) | Optimization method of plug-in type pure electric bus charging plan considering incomplete charging |
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 |