CN115204442A - Power grid-charging operator collaborative operation optimization method and system - Google Patents

Power grid-charging operator collaborative operation optimization method and system Download PDF

Info

Publication number
CN115204442A
CN115204442A CN202210465081.7A CN202210465081A CN115204442A CN 115204442 A CN115204442 A CN 115204442A CN 202210465081 A CN202210465081 A CN 202210465081A CN 115204442 A CN115204442 A CN 115204442A
Authority
CN
China
Prior art keywords
charging
optimization
operator
model
power grid
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
CN202210465081.7A
Other languages
Chinese (zh)
Inventor
李涛
雷才嘉
岑海凤
陈坤
孙开元
许苑
林琳
曾慧
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.)
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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 Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority to CN202210465081.7A priority Critical patent/CN115204442A/en
Publication of CN115204442A publication Critical patent/CN115204442A/en
Pending legal-status Critical Current

Links

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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • 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
    • G06Q10/06313—Resource planning in a project environment
    • 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
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/02—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from ac mains by converters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Power Engineering (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a power grid-charging operator cooperative operation optimization method and system, which describe heterogeneity and incompleteness of user decision behaviors based on a discrete selection model and verify the importance of information release on charging guidance. On the basis, the user behavior is guided through the comprehensive optimization of the charging price data and the charging capacity data, the space demand response capability of the charging load of the electric automobile is effectively explored, the power supply cost of a power grid is reduced through source-load interaction, and the potential of collaborative optimization is proved. And finally, considering the profit loss of the charging operator, and realizing reasonable distribution of net profit of cooperation of the power grid and the operator based on a Nash negotiation game mechanism.

Description

Power grid-charging operator collaborative operation optimization method and system
Technical Field
The invention belongs to the technical field of public charging, and particularly relates to a power grid-charging operator collaborative operation optimization method and system.
Background
With the support of fast charging technology, some devices, such as electric vehicles, can fulfill the basic charging requirement within half an hour, and therefore are usually ready to charge and go. From a traffic trip perspective, charging of a user of the fast-charging electric vehicle is correlated with a route decision. From the charging load angle, because the vehicle can go to different charging stations and charge, the access position of charging load is more nimble, demonstrates the characteristics that the space can be translated. Thus, the popularity of the fast charging mode will result in tighter coupling of the power network to the traffic network. On the one hand, the charge price of a fast charging station will affect the travel behavior of the electric vehicle driver. On the other hand, traffic conditions also affect the routing of the electric vehicle users, thereby changing the spatiotemporal distribution of their charging loads. If reasonable guidance is lacked, unreasonable distribution of charging load space may cause risks such as local overload and voltage overrun of a power grid, and threatens safe and stable operation of a power system.
At present, many existing technologies adopt different price driving signals, and indirectly affect the driving route and charging position decision of a user by changing the comprehensive driving cost (time, charging expense and the like) of the user, so as to achieve ideal traffic flow-charging load distribution and explore the space demand response potential of the rapid charging electric automobile. In addition, some of the prior art also explores the guiding potential of other price signals, such as: in order to transfer the charging load of the electric automobile, the influence of random charging and movement of the electric automobile on a coupling electric power-traffic system is relieved by setting node time-of-use electricity price and road congestion fee.
However, the above studies have been insufficient: existing research often assumes that charging station related prices are directly regulated by the grid, or that charging stations are affiliated with the grid. In fact, some charging stations are owned by the operator, and their self-profit charging price setting is contrary to the goal of the grid company. The charging price is an important component of the charging cost of the user and is an effective means for guiding the charging load. If the collaborative operation optimization of the power grid company and the charging operator can be realized, the demand response capability of the electric automobile load can be fully exerted, the moisture distribution of the power grid is improved, and the power grid operation cost is reduced. Considering that a power grid and a charging operator serve as different benefit subjects, the basis of cooperative operation is a reasonable cooperation mechanism, and therefore an effective revenue distribution scheme needs to be provided urgently to ensure the stability of the power grid-operator cooperation alliance.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art and provide a power grid-charging operator cooperative operation optimization method and system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in one aspect of the present invention, a power grid-charging operator collaborative operation optimization method is provided, including the following steps:
under the collaborative operation optimization scene, adjusting charging price data and charging capacity data specifically comprises the following steps: establishing a power grid-charging operator cooperative operation optimization model; determining optimized charging station price and charging capacity data according to the optimization model;
the net income data generated by the cooperative operation is optimally decomposed and distributed by adjusting the charging price data and the charging capacity data, so that the power supply cost data saved by the power grid through the cooperation and the income data increased by the charging operator through the cooperative operation are both improved.
As a preferred technical solution, the grid-charging operator collaborative operation optimization model is as follows:
Figure BDA0003623602520000021
Figure BDA0003623602520000022
wherein, F E Cost of power supply to the grid, F C For charging operation income, τ a Is a virtual charging road, a belongs to A C Corresponding to the charging price of the charging station,
Figure BDA0003623602520000031
solving the optimization model to obtain the power supply cost of the power grid under the cooperative operation scene for the capacity of the charging road
Figure BDA0003623602520000032
And charging operation cost under cooperative operation scene
Figure BDA0003623602520000033
The charging operator targets maximum charging operation income, which is F C The maximum optimization model of (a) is:
Figure BDA0003623602520000034
Figure BDA0003623602520000035
wherein x is a For traffic flow on road a, E ev x a Charging electric vehicleElectrical load, E ev The average charged energy of the vehicle is, a τand
Figure BDA0003623602520000036
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost under the initial scene
Figure BDA0003623602520000037
And the charging load E of the electric automobile ev x a 。
As a preferred technical solution, the determining, according to the optimization model, the optimized charging station price and charging capacity data specifically includes:
iterative solution is carried out on the power grid-charging operator collaborative operation optimization model by adopting a particle swarm algorithm to determine optimized charging station price and charging capacity data, and the method specifically comprises the following steps:
calling an optimization solver to solve the nonlinear optimization of the random user balance model to obtain charging operation income and charging load;
calling an optimization solver to solve second-order cone optimization of the alternating current optimal power flow model to obtain power supply cost;
updating the historical optimal positions of the particles and the historical optimal positions of the particle swarms, and improving the objective function until the improvement degree of the objective function is less than 0.01 percent;
finally, the power supply cost and the charging operation cost of the power grid in the collaborative operation scene are obtained;
when the particle swarm algorithm is adopted to carry out iterative solution on the power grid-charging operator collaborative operation optimization model, the solution efficiency is improved by starting parallel computation.
As a preferred technical solution, the nash negotiation game model is used to perform optimized decomposition on net revenue data generated by cooperative operation, and the nash negotiation game model specifically includes the following steps:
Figure BDA0003623602520000041
wherein the content of the first and second substances,
Figure BDA0003623602520000042
for the power supply cost of the power grid in the cooperative operation scene,
Figure BDA0003623602520000043
for the charging operation cost in the scenario of cooperative operation,
Figure BDA0003623602520000044
for the cost of power supply to the grid in the initial scenario,
Figure BDA0003623602520000045
for the charge running cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and an operator;
the Nash negotiation game model ensures individual rational and pareto optimal constraints as follows:
Figure BDA0003623602520000046
Figure BDA0003623602520000047
Figure BDA0003623602520000048
and carrying out convex optimization analysis on the Nash negotiation game model through an optimization solver.
In another aspect of the present invention, a power grid-charging operator collaborative operation optimization system is provided, which includes an adjustment module and a profit allocation module;
the adjusting module is used for adjusting the charging price data and the charging capacity data according to a scheduling protocol of a charging operator and a power grid in a collaborative operation optimization scene;
the income distribution module is used for optimizing and decomposing net income data generated by collaborative operation by adjusting charging price data and charging capacity data, so that power supply cost data saved by the power grid through collaborative operation and income data increased by a charging operator through collaborative operation are both improved.
As a preferred technical solution, the adjusting module specifically includes:
the optimization model establishing submodule is used for establishing a power grid-charging operator cooperative operation optimization model;
and the optimization model solving submodule is used for determining optimized charging station price and charging capacity data according to the optimization model.
As a preferred technical solution, the grid-charging operator collaborative operation optimization model is as follows:
Figure BDA0003623602520000049
Figure BDA0003623602520000051
wherein, F E Cost of power supply to the grid, F C For charging operation income, τ a Is a virtual charging road a ∈ A C Corresponding to the charging price of the charging station,
Figure BDA0003623602520000052
solving the optimization model to obtain the power supply cost of the power grid under the cooperative operation scene for the capacity of the charging road
Figure BDA0003623602520000053
And charge operation cost in a collaborative operation scenario
Figure BDA0003623602520000054
The charging operator targets maximum charging operation income, which is F C The maximum optimization model of (a) is:
Figure BDA0003623602520000055
Figure BDA0003623602520000056
wherein x is a For traffic flow on road a, E ev x a Charging the electric vehicle with a load, E ev The average charged energy of the vehicle is, a τand
Figure BDA0003623602520000057
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost in the initial scene
Figure BDA0003623602520000058
And the charging load E of the electric automobile ev x a 。
As an optimal technical scheme, when the optimization model is analyzed, the optimization model solving submodule adopts a particle swarm algorithm to perform iterative solution on the power grid-charging operator cooperative operation optimization model so as to determine optimized charging station price and charging capacity data.
As a preferred technical solution, the optimization model solving submodule specifically includes:
calling an optimization solver to solve the nonlinear optimization of the random user balance model to obtain charging operation income and charging load;
calling an optimization solver to solve second-order cone optimization of the alternating current optimal power flow model to obtain power supply cost;
updating the historical optimal positions of the particles and the historical optimal positions of the particle swarms, and improving the objective function until the improvement degree of the objective function is less than 0.01 percent;
finally, power supply cost and charging operation cost of the power grid under the collaborative operation scene are obtained;
when the optimization model solving submodule operates, parallel computing is started to improve solving efficiency.
As a preferred technical solution, the nash negotiation game model is used to perform optimized decomposition on net revenue data generated by cooperative operation, and the nash negotiation game model specifically includes the following steps:
Figure BDA0003623602520000061
wherein the content of the first and second substances,
Figure BDA0003623602520000062
for the power supply cost of the power grid in the cooperative operation scene,
Figure BDA0003623602520000063
for the charging operation cost in the scenario of cooperative operation,
Figure BDA0003623602520000064
for the cost of power supply to the grid in the initial scenario,
Figure BDA0003623602520000065
for the charge running cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and the operator;
the Nash negotiation game model ensures individual rational and pareto optimal constraints as follows:
Figure BDA0003623602520000066
Figure BDA0003623602520000067
Figure BDA0003623602520000068
and performing convex optimization analysis on the Nash negotiation game model through an optimization solver.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) The power grid-charging operator cooperative operation optimization system provided by the invention describes heterogeneity and incompleteness of user decision behaviors based on a discrete selection model, and verifies the importance of information release on charging guidance. On the basis, the user behavior is guided through the comprehensive optimization of the charging price data and the charging capacity data, the space demand response capability of the charging load of the electric automobile is effectively explored, the power supply cost of a power grid is reduced through source-load interaction, and the potential of collaborative optimization is proved. And finally, considering the profit loss of the charge operator, and realizing reasonable distribution of net profit of cooperation of the power grid and the operator based on a Nash negotiation game mechanism.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 shows an extended network model of a charging station of an embodiment of the invention;
fig. 2 shows a flow chart of a grid-charging operator collaborative operation optimization method according to an embodiment of the invention;
FIG. 3 shows a flow of a co-operating optimization solution according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Examples
The invention provides a power grid-charging operator cooperative operation optimization architecture, which explores the flexibility of rapid charging load through the setting of charging price data and charging capacity data of a charging station. The user decision behavior is modeled based on the discrete selection model, so that the heterogeneity and incompleteness of the user are depicted. And on the basis of comprehensive optimization of charging price data and charging capacity data, starting from two aspects of time cost and charging cost, guidance of vehicle traveling and charging behaviors is achieved. And finally, based on a cooperative game mechanism of Nash negotiation, a profit distribution scheme of a power grid and a charging operator is provided, and the stability of a cooperative alliance is ensured.
The urban traffic network can be described as a connected directed graph G T And (N, A), wherein the node set N represents each intersection in the traffic network, and the directed edge set A represents a road in the traffic network (the up and down lanes are represented by two parallel and reverse edges). The traffic flow on a road a in a traffic network is defined as the number of vehicles passing a certain section per unit time in units of vehicles per hour, similarly to the concept of current. Travel demands of travel users in the traffic network can be clustered into a plurality of origin-destination (O-D) pairs to form a travel demand set W belonging to W. Each O-D pair W epsilon W comprises a starting point r, an end point s and a traffic flow D w And (5) ternary information. The topology of each path may be determined by a path-branch correlation coefficient delta ar The following steps are described: if the path R belongs to R w Passing road a belongs to A, then delta ar And =1. If not, delta ar And =0. The traffic flow on path r of O-D vs w is denoted as f rw 。
Vehicles operating in the transportation network may be classified as vehicles with a rapid charging demand or as vehicles without a rapid charging demand. In order to take account of the process of charging and queuing vehicles going to a charging station, an extended network model as shown in fig. 1 is adopted in modeling.
In the conventional road set A R On the basis of (A), a virtual charging road set A is introduced C Representing the queuing and charging behavior of vehicles at the charging station, while introducing a virtual bypass road set A B Indicating that other vehicles have not entered the charging station. Suppose that a vehicle with a rapid charge demand needs to be charged once on the road, i.e., a route is selected that passes through an overcharged road. Due to the limited road capacity data, roads of cities have a congestion characteristic, i.e., the road traffic time increases as the traffic flow rises. Similarly, because charging stations have limited charging piles, queuing times increase as traffic flow to the charging station increases, and a similar modeling approach may be used. Passage time t of each road a The function is as follows:
Figure BDA0003623602520000081
wherein x is a In order to be the traffic flow on the road a,
Figure BDA0003623602520000082
the free flow time for passing/completing charging, i.e., the transit time when the road flow rate is 0,
Figure BDA0003623602520000083
and
Figure BDA0003623602520000084
and the capacity data of the ordinary road and the charging road.
Macroscopic traffic flows need to meet the following basic constraints:
Figure BDA0003623602520000085
Figure BDA0003623602520000091
Figure BDA0003623602520000092
wherein d is w For all paths of traffic on O-D vs w, f rw Representing the traffic flow, x, on a path r of O-D vs w a For traffic flow in equilibrium, delta ar Is the path-branch correlation coefficient; the formula (2) represents the conservation of flow, namely the sum of the flow of each path w and the travel demand of the O-D pair is equal; expression (3) represents the association relationship between the road and the route; equation (4) is a path flow non-negative constraint.
The formation of the macroscopic traffic flow is the result of the decision of various types of users going out from a microscopic angle. The travel user tends to select the path with the lowest travel cost for travel:
Figure BDA0003623602520000093
i.e. the toll c of the route rw Expressed as the sum of the costs of the roads it travels. In the extended traffic network, the traffic charges ca for each road are expressed as follows:
Figure BDA0003623602520000094
wherein, tau a Is that the virtual charging road a belongs to A C Charging price corresponding to charging station, A C For a set of virtual charging roads, E ev The average charging energy of the vehicle. Gamma is the value per commute time. As the traffic flow on a road increases, the time and expense of passing the road also increases. Therefore, users on a trip face the interaction of two mechanisms of the shortest-circuit decision and the congestion characteristic of the road.
Considering the observed deviation and the decision randomness of the user, the discrete selection model is adopted to model the decision. In the decision making process, the utility of O-D to the path r of w perceived by the user is expressed as:
Figure BDA0003623602520000095
where θ is a perceptual error parameter, c rw For passing through the route, i.e. the actual passing through, U rw For the utility, xi, perceived by the user rw Is a random error term for the path. All users will select the path with the greatest perceptual utility (i.e., lowest perceptual cost) given the network parameters and price signature. When no user can unilaterally reduce the perception cost by changing the path selection, the traffic flow reaches a random user equilibrium state. Based on the logit discrete selection model, assuming that the random error term satisfies the independent same distribution condition, the probability of the user selecting the path r is expressed as:
Figure BDA0003623602520000101
U jw the user perceived utility of 0-D to path j of w, c jw The actual toll for path j of 0-D versus w. It should be noted that θ > 0 is a given parameter that characterizes the traveler's varying degrees of perception of the cost of travel. A larger value of θ indicates a smaller perception error, and thus the driver is more likely to select the route with the lowest cost. The probability of selecting the path r by the user calculated according to the formula (8) can reflect the influence of the current charging price data on the selection of the user, and the rationality of the current charging station price is reflected on the side surface.
According to the above model, the random user equilibrium state can be expressed as:
Figure BDA0003623602520000102
the traffic flow x in the above balanced state a The distribution can be solved by the following equivalent optimization model:
Figure BDA0003623602520000103
s.t(1)-(6) (10)
wherein, F T An objective function is optimized for equivalence of equilibrium states.
Urban distribution networks typically operate in a radial open loop, which can be described as a tree-based connectivity graph G E (M, B). Wherein, the node set M represents a bus in the power distribution network, and the branch set B represents a line in the power distribution network. The number of the bus corresponding to the root node is 0, and a head node and a tail node (i, j) are adopted to represent each branch, wherein i is a line head node, and j is a line tail node. Based on the second-order cone relaxation, the following alternating current power flow model can be established:
Figure BDA0003623602520000104
Figure BDA0003623602520000105
Figure BDA0003623602520000106
Figure BDA0003623602520000111
wherein, P ij And Q ij Respectively an active power flow and a reactive power flow flowing through the head end of the line ij; r ij And X ij Respectively the resistance and reactance, P, of the line ij jk And Q jk Active power flow and reactive power flow p flowing through the head end of a subordinate line jk of a terminal node j of a line ij j And q is j Respectively an active load and a reactive load of a node j; v. of i Is the square term of the voltage at node i, v j Is the squared term of the voltage at node j, v 0 Which is the square of the root node voltage,L ij is the current squared term, Z, of line ij ij Is the impedance of line ij. Equations (11) and (12) represent node active power and reactive power balance conditions, respectively. Equation (13) shows the voltage drop relationship of each node. Equation (14) represents the second order cone relaxation.
To the left of the constraint (13) equation, the active power injection at node j includes active power generation
Figure BDA0003623602520000112
Base load
Figure BDA0003623602520000113
And charging load E of electric vehicle ev x a Wherein the charging load of the electric automobile is in direct proportion to the traffic flow of the virtual charging road:
Figure BDA0003623602520000114
wherein, M FCS Representing a node subset with charging stations connected, corresponding to a e A in the expansion diagram C . On the basis of the alternating current power flow model, the following safety constraints are further introduced:
Figure BDA00036236025200001112
Figure BDA0003623602520000115
wherein the content of the first and second substances,
Figure BDA0003623602520000116
for the active generated power of node j in the distribution network,
Figure BDA0003623602520000117
and
Figure BDA0003623602520000118
are respectively nodesThe lower limit and the upper limit of the active generated power of j,v j and with
Figure BDA0003623602520000119
The lower limit and the upper limit of the square of the voltage of the node j are shown in the formula (16), the active power generation upper limit and the active power generation lower limit of the node are shown in the formula (17), and the voltage of the node is shown in the formula (17).
Given the common load and charging load at each point, the power system scheduling will target minimizing the cost of the energy production assembly, i.e., power generation scheduling
Figure BDA00036236025200001110
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00036236025200001111
distributed Power Generation cost for node j, a 1j 、a 2j Respectively, a distributed generation cost coefficient, lambda, of a node j in the distribution network 0 ∑ k：(0，k)∈B P 0k Cost of electricity to purchase from the grid, λ 0 For power rates of the transmission network protocol, P 0k For the active power flow flowing through the head end of the line 0k, 0 represents the root node.
On the basis of fusion modeling, a power grid-charging operator collaborative operation optimization model is established, joint optimization of charging station price and charging capacity data is achieved, and charging station selection of a charging user is guided. And based on a cooperative game theory, a reasonable profit allocation mechanism is established, and the stability of a power grid-charging operator cooperative alliance is ensured.
As shown in fig. 2, in an embodiment of the present invention, the process of establishing and solving the co-operation optimization model is as follows:
at present, the electricity price of the charging station generally adopts a time-of-use electricity price mechanism, and the time-of-use electricity price is fixed in a period of time and is independent of the load access position. And the service charge of each charging station can be adjusted. In the initial scenario, the charging operator does not cooperate with the grid. And the charging operator carries out price optimization with the maximum operation income as a target:
Figure BDA0003623602520000121
Figure BDA0003623602520000122
wherein x is a For traffic flow on road a, E ev x a Charging the electric vehicle with a load, E ev The average charged energy of the vehicle is, a τand
Figure BDA0003623602520000123
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost in the initial scene
Figure BDA0003623602520000124
And the charging load E of the electric automobile ev x a . And (5) solving an optimization model formed by (11) to (18) according to the charging load during power grid dispatching to obtain the power supply cost under the initial scene
Figure BDA0003623602520000125
Under the cooperative operation optimization scene, a charging operator and a power grid schedule reach an agreement, the charging price data and the charging capacity data are adjusted together, the charging station selection of a charging user is guided, and the power grid is assisted to reduce the power supply cost while the operation income is increased. The optimization model is as follows:
Figure BDA0003623602520000126
s.t(1)-(6)，(9)，(13)-(17)
Figure BDA0003623602520000127
wherein, F E Cost of power supply to the grid (objective function of equation 18), F C For charging operation revenue (objective function of equation 19), τ a Is a virtual charging road a ∈ A C Corresponding to the charging price of the charging station,
Figure BDA0003623602520000131
for the capacity of the charging road, in the calculation process, for tau a And
Figure BDA0003623602520000132
and (6) carrying out variable optimization. Solving the optimization model to obtain the power supply cost of the power grid under the cooperative operation scene
Figure BDA0003623602520000133
And charge operation cost in a collaborative operation scenario
Figure BDA0003623602520000134
The solution of the model has the following characteristics:
1) The model structure is double-layer optimization of a power grid-charging operator, a solving target relates to a non-convex bilinear function, direct solving relates to high-dimensional mixed integer programming, and the calculation complexity is high.
2) Historical information such as the charging demand of the user is mainly mastered on the charging operator side, and the random user balance model and the communication optimal power flow model are preferably solved respectively in consideration of the privacy of the user information.
3) The problem has the key characteristic of 'sequential efficient calculation': namely, a group of charging price data signals are determined, rapid evaluation and calculation can be sequentially carried out on all key states such as random user equilibrium state, traffic flow distribution of a traffic network, power flow distribution of the power network and the like, and all links are convex optimization.
In view of the above features, a Particle Swarm Optimization (PSO) is selected to iteratively solve the collaborative Optimization model. The algorithm is a random optimization technique for simulating the collective behavior of biological populations, and each individual in the population continuously learns the experience of the individual and other individualsAnd improving and searching the position of the mobile phone. For the problem, each particle individual represents a group of price and capacity allocation schemes of each charging station, and the fitness corresponds to the difference between the power supply cost of the power grid and the charging operation income under the scheme (namely the value of the objective function (20)). In the fitness evaluation, calling an lpopt nonlinear optimization solver to solve nonlinear optimization (10) of the random user balance model to obtain charging operation income and charging load; and calling a Gurobi solver to solve second-order cone optimization (18) of the alternating current optimal power flow model to obtain power supply cost. Then, updating the historical optimal positions of the particles and the historical optimal positions of the particle swarm, improving the objective function until the termination standard is reached (the improvement degree of the objective function is less than 0.01 percent), and finally obtaining the power supply cost of the power grid in the collaborative operation scene
Figure BDA0003623602520000135
And charging operation cost under cooperative operation scene
Figure BDA0003623602520000136
The solution flow is shown in fig. 3.
The particle swarm algorithm has parallelism, and for the problem, the quantitative calculation of different pricing strategy effects can be accelerated under the parallel action of a multi-core CPU or a GPU. The PSO solution is carried out based on the MATLAB toolbox, and the solution efficiency is improved by starting parallel computation.
In one embodiment of the invention, the process of establishing and solving the revenue distribution model is as follows:
the power grid power supply cost under the cooperative operation scene is obtained by solving the cooperative operation optimization model
Figure BDA0003623602520000141
And charging operation cost
Figure BDA0003623602520000142
The minimization of the difference between the two is ensured. From the perspective of cooperative gaming, the alliance of the power grid and the charging operator obtains the collective benefit, but each member still obtainsAnd (4) the method is self-benefited. However, in the absence of a reasonable revenue distribution mechanism, the grid and charging operators do not necessarily obtain a larger net revenue, which affects the aggressiveness of the operators to participate in the collaborative operation optimization. Therefore, a reasonable revenue distribution mechanism needs to be established, so that both the power grid and the operator can obtain the increase of the revenue. In consideration of the difference of the attributes of a power grid and a charging operator, a Nash negotiation game model is adopted in the section, and net earnings generated by cooperation are distributed in two parties, so that the individual rationality and pareto optimization between a power grid company and the charging operator are met, and the stability of the cooperation union of the two parties is ensured. The implication of negotiation is to find a point in the set of revenue vectors under different allocations, so that the net revenue of both parties is the farthest from the worst net revenue (i.e., the net revenue of the initial non-collaborative operation optimization scenario). In particular, the problem is to maximize the product of the power supply cost saved by the grid company through the coordination and the income increased by the charging operator through the coordination:
Figure BDA0003623602520000143
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003623602520000144
for the cost of power supply to the grid in a coordinated operation scenario,
Figure BDA0003623602520000145
for the charging operation cost in the scenario of cooperative operation,
Figure BDA0003623602520000146
for the cost of power supply to the grid in the initial scenario,
Figure BDA0003623602520000147
for the charge running cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and the operator;
constraints that guarantee the psychology of individuals and pareto optima are as follows:
Figure BDA0003623602520000148
Figure BDA0003623602520000149
Figure BDA00036236025200001410
wherein, C int And distributing values for the transfer of net income between the power grid and the operator. And solving the optimal solution (namely Nash equilibrium solution) of the negotiation problem to obtain the optimal net income distribution value between the power grid and the charging operator. Equation (22) can also be converted to a logarithmic form as follows, and solved by convex optimization using a Gurobi solver.
Figure BDA0003623602520000151
In another embodiment of the present application, a power grid-charging operator collaborative operation optimization system is further provided, which is applicable to the power grid-charging operator collaborative operation optimization method of the foregoing embodiment, and includes an adjustment module and a profit allocation module;
(1) The adjusting module is used for adjusting the charging price data and the charging capacity data according to a scheduling protocol of a charging operator and a power grid in a collaborative operation optimization scene, and specifically comprises the following steps:
(1.1) an optimization model establishing submodule for establishing a power grid-charging operator collaborative operation optimization model, which comprises the following steps:
Figure BDA0003623602520000152
Figure BDA0003623602520000153
wherein, F E Cost of power supply to the grid, F C For charging operation income, τ a Is that the virtual charging road a belongs to A C Corresponding to the charging price of the charging station,
Figure BDA0003623602520000154
solving the optimization model to obtain the power supply cost of the power grid in the collaborative operation scene for the capacity of the charging road
Figure BDA0003623602520000155
And charge operation cost in a collaborative operation scenario
Figure BDA0003623602520000156
Charging operator targets maximum charging operation income, which is F C The maximum optimization model of (2) is:
Figure BDA0003623602520000157
Figure BDA0003623602520000158
wherein x is a For traffic flow on road a, E ev x a Charging the electric vehicle with a load, E ev The average charged energy of the vehicle is, a τand
Figure BDA0003623602520000159
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost under the initial scene
Figure BDA0003623602520000161
And the charging load E of the electric automobile ev x a 。
(1.2) the optimization model solving submodule is used for determining optimized charging station price and charging capacity data according to the optimization model, and specifically comprises the following steps:
calling an optimization solver to solve the nonlinear optimization of the random user balance model to obtain charging operation income and charging load;
calling an optimization solver to solve second-order cone optimization of the alternating current optimal power flow model to obtain power supply cost;
updating the historical optimal positions of the particles and the historical optimal positions of the particle swarms, and improving the objective function until the improvement degree of the objective function is less than 0.01 percent;
finally, the power supply cost and the charging operation cost of the power grid in the collaborative operation scene are obtained;
when the optimization model solving submodule operates, parallel computing is started to improve solving efficiency.
(2) The income distribution module is used for optimizing and decomposing net income data generated by collaborative operation by adjusting charging price data and charging capacity data, so that power supply cost data saved by the power grid in collaboration and income data increased by a charging operator in collaboration are both improved.
Carrying out optimized decomposition on net income data generated by cooperative operation by adopting a Nash negotiation game model, wherein the Nash negotiation game model is as follows:
Figure BDA0003623602520000162
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003623602520000163
for the cost of power supply to the grid in a coordinated operation scenario,
Figure BDA0003623602520000164
for the charging operation cost in the scenario of cooperative operation,
Figure BDA0003623602520000165
for the cost of power supply to the grid in the initial scenario,
Figure BDA0003623602520000166
for the charge running cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and an operator;
the Nash negotiation game model ensures individual rational and pareto optimal constraints as follows:
Figure BDA0003623602520000167
Figure BDA0003623602520000168
Figure BDA0003623602520000169
and carrying out convex optimization analysis on the Nash negotiation game model through an optimization solver.
The power grid-charging operator collaborative operation optimization system provided by the invention describes heterogeneity and incompleteness of user decision behaviors based on a discrete selection model, and verifies importance of information distribution on charging guidance. On the basis, the behavior of the user is guided through the comprehensive optimization of the charging price data and the charging capacity data, the space demand response capability of the charging load of the electric automobile is effectively explored, the power supply cost of a power grid is reduced through source-load interaction, and the potential of collaborative optimization is proved. And finally, considering the loss of interest giving of a charging operator, and realizing reasonable distribution of net profit of cooperation of the power grid and the operator based on a Nash negotiation game mechanism.
In addition, it needs to be further explained that the collaborative optimization system can be further expanded to a scene of considering traffic demand change of each time period in a day, more accurate traffic dynamic modeling is introduced, and application of the system in scheduling in the day ahead and rolling each time scale in the day is realized. Meanwhile, in consideration of benefit subjects, the charging system can be further expanded to more realistic scenes that a plurality of charging operators compete with one another and part of charging stations belong to a power grid company.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (10)

1. A power grid-charging operator collaborative operation optimization method is characterized by comprising the following steps:
under the collaborative operation optimization scene, adjusting charging price data and charging capacity data, specifically: establishing a power grid-charging operator collaborative operation optimization model; determining optimized charging station price and charging capacity data according to the optimization model;
the net income data generated by the cooperative operation is optimally decomposed and distributed by adjusting the charging price data and the charging capacity data, so that the power supply cost data saved by the power grid through the cooperation and the income data increased by the charging operator through the cooperative operation are both improved.
2. The grid-charging operator collaborative operation optimization method according to claim 1, wherein the grid-charging operator collaborative operation optimization model is as follows:
Figure FDA0003623602510000011
Figure FDA0003623602510000012
wherein, F E Cost of power supply to the grid, F C For charging operation income, τ a Is a virtual charging road, a belongs to A C Charging price corresponding to charging stationThe number of the grids is equal to or less than the number of the grids,
Figure FDA0003623602510000013
solving the optimization model to obtain the power supply cost of the power grid under the cooperative operation scene for the capacity of the charging road
Figure FDA0003623602510000014
And charging operation cost under cooperative operation scene
Figure FDA0003623602510000015
Charging operator targets maximum charging operation income, which is F C The maximum optimization model of (2) is as follows:
Figure FDA0003623602510000016
Figure FDA0003623602510000017
wherein x is a For traffic flow on road a, E ev x a Charging the electric vehicle with a load, E ev The average charged energy of the vehicle is, a τand
Figure FDA0003623602510000018
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost in the initial scene
Figure FDA0003623602510000019
And the charging load E of the electric automobile ev x a 。
3. The method for optimizing power grid-charging operator collaborative operation according to claim 1, wherein the determining of the optimized charging station price and charging capacity data according to the optimization model specifically includes:
iterative solution is carried out on the power grid-charging operator collaborative operation optimization model by adopting a particle swarm algorithm to determine optimized charging station price and charging capacity data, and the method specifically comprises the following steps:
calling an optimization solver to solve the nonlinear optimization of the random user balance model to obtain charging operation income and charging load;
calling an optimization solver to solve second-order cone optimization of the alternating current optimal power flow model to obtain power supply cost;
updating the historical optimal positions of the particles and the historical optimal positions of the particle swarms, and improving the objective function until the improvement degree of the objective function is less than 0.01 percent;
finally, the power supply cost and the charging operation cost of the power grid in the collaborative operation scene are obtained;
when the particle swarm algorithm is adopted to carry out iterative solution on the power grid-charging operator collaborative operation optimization model, the solution efficiency is improved by starting parallel computation.
4. The power grid-charging operator cooperative operation optimization method according to claim 1, wherein the nash negotiation game model is adopted to perform optimized decomposition on net income data generated by cooperative operation, and the nash negotiation game model is specifically as follows:
Figure FDA0003623602510000021
wherein the content of the first and second substances,
Figure FDA0003623602510000022
for the cost of power supply to the grid in a coordinated operation scenario,
Figure FDA0003623602510000023
for the charging operation cost in the scenario of cooperative operation,
Figure FDA0003623602510000024
for the cost of power supply to the grid in the initial scenario,
Figure FDA0003623602510000025
for the charge running cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and the operator;
the Nash negotiation game model ensures the individual rational and pareto optimal constraint, which is as follows:
Figure FDA0003623602510000026
Figure FDA0003623602510000027
Figure FDA0003623602510000028
and carrying out convex optimization analysis on the Nash negotiation game model through an optimization solver.
5. A power grid-charging operator collaborative operation optimization system is characterized by comprising an adjusting module and a profit distribution module;
the adjusting module is used for adjusting charging price data and charging capacity data according to a scheduling protocol of a charging operator and a power grid under a collaborative operation optimization scene;
the income distribution module is used for optimizing and decomposing net income data generated by collaborative operation by adjusting the charging price data and the charging capacity data, so that the power supply cost data saved by the power grid through collaborative operation and the income data increased by the charging operator through collaborative operation are both improved.
6. The grid-charging operator collaborative operation optimization system according to claim 5, wherein the adjusting module specifically includes:
the optimization model establishing submodule is used for establishing a power grid-charging operator cooperative operation optimization model;
and the optimization model solving submodule is used for determining optimized charging station price and charging capacity data according to the optimization model.
7. The grid-charging operator collaborative operation optimization system according to claim 6, wherein the grid-charging operator collaborative operation optimization model is as follows:
Figure FDA0003623602510000031
Figure FDA0003623602510000032
wherein, F E Cost of power supply to the grid, F C For charging operation income, τ a Is a virtual charging road a ∈ A C Corresponding to the charging price of the charging station,
Figure FDA0003623602510000033
solving the optimization model to obtain the power supply cost of the power grid under the cooperative operation scene for the capacity of the charging road
Figure FDA0003623602510000034
And charge operation cost in a collaborative operation scenario
Figure FDA0003623602510000035
Charging operator targets maximum charging operation income, which is F C The maximum optimization model of (2) is:
Figure FDA0003623602510000036
Figure FDA0003623602510000037
wherein x is a For traffic flow on road a, E ev x a Charging the electric vehicle with a load, E ev The average charged energy of the vehicle is, a τand
Figure FDA0003623602510000038
respectively the upper limit and the lower limit of the charging price; solving the optimization model to obtain the charging operation cost in the initial scene
Figure FDA0003623602510000039
And the charging load E of the electric automobile ev x a 。
8. The power grid-charging operator collaborative operation optimization system according to claim 6, wherein the optimization model solving submodule adopts a particle swarm algorithm to perform iterative solution on the power grid-charging operator collaborative operation optimization model to determine optimized charging station price and charging capacity data when analyzing the optimization model.
9. The power grid-charging operator collaborative operation optimization system according to claim 6, wherein the optimization model solving submodule specifically includes:
calling an optimization solver to solve the nonlinear optimization of the random user balance model to obtain charging operation income and charging load;
calling an optimization solver to solve second-order cone optimization of the alternating current optimal power flow model to obtain power supply cost;
updating the historical optimal positions of the particles and the historical optimal positions of the particle swarms, and improving the objective function until the improvement degree of the objective function is less than 0.01 percent;
finally, the power supply cost and the charging operation cost of the power grid in the collaborative operation scene are obtained;
when the optimization model solving submodule operates, parallel computing is started to improve solving efficiency.
10. The grid-charging operator cooperative operation optimization system according to claim 5, wherein a Nash negotiation gaming model is adopted to perform optimized decomposition on net income data generated by cooperative operation, and the Nash negotiation gaming model is specifically as follows:
Figure FDA0003623602510000041
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003623602510000042
for the power supply cost of the power grid in the cooperative operation scene,
Figure FDA0003623602510000043
for the charging operation cost in the scenario of cooperative operation,
Figure FDA0003623602510000044
for the cost of power supply to the grid in the initial scenario,
Figure FDA0003623602510000045
for the charging operation cost in the initial scenario, C int Allocating a value for the transfer of net income between the power grid and the operator;
the Nash negotiation game model ensures individual rational and pareto optimal constraints as follows:
Figure FDA0003623602510000046
Figure FDA0003623602510000047
Figure FDA0003623602510000048
and carrying out convex optimization analysis on the Nash negotiation game model through an optimization solver.
CN202210465081.7A 2022-04-29 2022-04-29 Power grid-charging operator collaborative operation optimization method and system Pending CN115204442A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210465081.7A CN115204442A (en) 2022-04-29 2022-04-29 Power grid-charging operator collaborative operation optimization method and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210465081.7A CN115204442A (en) 2022-04-29 2022-04-29 Power grid-charging operator collaborative operation optimization method and system

Publications (1)

Publication Number Publication Date
CN115204442A true CN115204442A (en) 2022-10-18

Family

ID=83575109

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210465081.7A Pending CN115204442A (en) 2022-04-29 2022-04-29 Power grid-charging operator collaborative operation optimization method and system

Country Status (1)

Country Link
CN (1) CN115204442A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115409294A (en) * 2022-11-01 2022-11-29 江西江投电力技术与试验研究有限公司 Robust optimization method for power distribution network scheduling and charging cooperation
CN116681269A (en) * 2023-08-03 2023-09-01 南京邮电大学 Intelligent collaborative operation optimization method for power grid interactive type efficient residential building

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115409294A (en) * 2022-11-01 2022-11-29 江西江投电力技术与试验研究有限公司 Robust optimization method for power distribution network scheduling and charging cooperation
CN116681269A (en) * 2023-08-03 2023-09-01 南京邮电大学 Intelligent collaborative operation optimization method for power grid interactive type efficient residential building
CN116681269B (en) * 2023-08-03 2023-10-13 南京邮电大学 Intelligent collaborative operation optimization method for power grid interactive type efficient residential building

Similar Documents

Publication Publication Date Title
Ucer et al. Modeling and analysis of a fast charging station and evaluation of service quality for electric vehicles
Luo et al. Charging scheduling strategy for different electric vehicles with optimization for convenience of drivers, performance of transport system and distribution network
Li et al. Intelligent vehicle-to-vehicle charging navigation for mobile electric vehicles via VANET-based communication
Yuan et al. Competitive charging station pricing for plug-in electric vehicles
CN112467722B (en) Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station
Asna et al. Analysis of an optimal planning model for electric vehicle fast-charging stations in Al Ain City, United Arab Emirates
Rigas et al. Managing electric vehicles in the smart grid using artificial intelligence: A survey
Ou et al. Investigating wireless charging and mobility of electric vehicles on electricity market
Qiu et al. Reinforcement learning for electric vehicle applications in power systems: A critical review
CN115204442A (en) Power grid-charging operator collaborative operation optimization method and system
Laha et al. Game theory based charging solution for networked electric vehicles: A location-aware approach
CN111091224A (en) Electric vehicle charging electric energy transaction method based on block chain technology
Sheng et al. Coordinated pricing of coupled urban power-traffic networks: The value of information sharing
Liu Dynamic response characteristics of fast charging station-evs on interaction of multiple vehicles
Graber et al. Two-stage stochastic sizing and packetized energy scheduling of BEV charging stations with quality of service constraints
Acha et al. Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks
Lv et al. Coordinating urban power-traffic networks: A subsidy-based Nash–Stackelberg–Nash game model
Huang et al. Investigation of electric vehicle smart charging characteristics on the power regulation performance in solar powered building communities and battery degradation in Sweden
Tao et al. Adaptive integrated planning of electricity networks and fast charging stations under electric vehicle diffusion
CN114896743A (en) Energy-traffic coupling modeling method and system considering user decision randomness
CN112686441A (en) Electric automobile charging navigation and path selection method based on traffic balance
Pan et al. Many-objective optimization for large-scale EVs charging and discharging schedules considering travel convenience
Affolabi et al. Hierarchical transactive energy scheduling of electric vehicle charging stations in constrained power distribution and transportation networks
Zhong et al. Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price
Zhao et al. Research on emergency distribution optimization of mobile power for electric vehicle in photovoltaic-energy storage-charging supply chain under the energy blockchain

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