CN114022046A - Comprehensive energy system optimal scheduling method considering traffic balance - Google Patents

Comprehensive energy system optimal scheduling method considering traffic balance Download PDF

Info

Publication number
CN114022046A
CN114022046A CN202111443026.XA CN202111443026A CN114022046A CN 114022046 A CN114022046 A CN 114022046A CN 202111443026 A CN202111443026 A CN 202111443026A CN 114022046 A CN114022046 A CN 114022046A
Authority
CN
China
Prior art keywords
load
vehicle
charging
time
road
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.)
Granted
Application number
CN202111443026.XA
Other languages
Chinese (zh)
Other versions
CN114022046B (en
Inventor
姜晓锋
魏巍
徐琳
李小鹏
刘畅
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.)
Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
Original Assignee
Electric Power Research Institute of State Grid Sichuan Electric Power 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 Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd filed Critical Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
Priority to CN202111443026.XA priority Critical patent/CN114022046B/en
Publication of CN114022046A publication Critical patent/CN114022046A/en
Application granted granted Critical
Publication of CN114022046B publication Critical patent/CN114022046B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06Q10/06—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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Power Engineering (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a comprehensive energy system optimization scheduling method considering traffic balance, which belongs to the field of traffic-power grid fusion operation, and realizes reduction of system peak-valley difference by introducing an electric vehicle V2G technology and a demand side response strategy; traffic network flow optimization is realized through traffic balance guidance, and road congestion is reduced; the operation cost is reduced by orderly charging and discharging of the electric automobile and the multi-energy coupling scheduling, the influence among electricity, gas, heat and cold load coupling devices is considered, and the optimal scheduling method of the comprehensive energy system with the aim of minimizing the operation cost is realized.

Description

Comprehensive energy system optimal scheduling method considering traffic balance
Technical Field
The invention belongs to the field of traffic-power grid fusion operation, and particularly relates to a comprehensive energy system optimal scheduling method considering traffic balance.
Background
In recent years, the rapid development of renewable energy technologies mainly based on wind turbines and photovoltaics provides a new solution for energy supply. Compared with the traditional energy system, the comprehensive energy system can effectively integrate different energy sources including renewable energy sources, and can perform coupling conversion in the links of energy production, transmission, distribution and consumption, thereby meeting the energy demand and improving the energy utilization efficiency and the operation economy. At present, research on the optimized operation of the comprehensive energy system mainly focuses on flexibly adjusting various energy forms and energy storage devices, and the new energy consumption capability and the operation economy of the system are improved by means of time-of-use electricity price, price compensation guidance and the like for carrying out cooperative scheduling. A combined demand response model can be provided in the multi-region level comprehensive energy system, and the overall demand side response capability of the multi-energy interconnection system is enhanced. A great deal of research verifies that the cooperative scheduling of the multi-energy system has incomparable advantages compared with the traditional scheduling model in the aspect of improving the system operation benefit.
With the gradual increase of the permeability of the electric automobile, the load demand of the comprehensive energy system can be influenced by the large-scale use of the electric automobile through the charging and discharging behaviors. In the early stage, most of charging models of electric vehicles are modeled based on statistical data or assumed data, the characteristic that the electric vehicles have space-time characteristics when going out cannot be reflected, the influence of traffic network flow on vehicle going out is ignored, and the mutual connection between a traffic network and a comprehensive energy system in actual life cannot be considered. With the progress of research, the spatial characteristics of the charging load of the electric automobile are also concerned, and related documents also consider the traffic attributes of the electric automobile, simulate the travel of the electric automobile by using a shortest path algorithm, but ignore the running condition that the electric automobile may be congested in an actual traffic network, and do not consider the influence of traffic congestion on the route selection and charging decision of the electric automobile user.
In conclusion, the comprehensive energy system considering traffic balance is a key problem of future traffic-power grid fusion operation, and domestic and foreign scholars conduct a great deal of research on interaction among power grids, natural gas networks and electric automobiles, but the research on the comprehensive energy system and the traffic network fusion operation is less, and the characteristics of the traffic-power grid fusion operation cannot be accurately simulated.
Disclosure of Invention
In order to solve the defects existing in the prior art, the invention aims to provide a comprehensive energy system optimization scheduling method considering traffic balance, which realizes the reduction of system peak-valley difference by introducing an electric vehicle V2G technology and a demand side response strategy; traffic network flow optimization is realized through traffic balance guidance, and road congestion is reduced; the operation cost is reduced by orderly charging and discharging of the electric automobile and the multi-energy coupling scheduling, the influence among electricity, gas, heat and cold load coupling devices is considered, and the optimal scheduling method of the comprehensive energy system with the aim of minimizing the operation cost is realized.
The technical scheme adopted by the invention is as follows:
step l: the park comprehensive energy system predicts load data and generator output data of each time period of the next day of the system according to historical load data and by taking 15 minutes to 1 hour as a scheduling interval, wherein the load data and the generator output data comprise an electrical load Le，tGas load Lg，tCold load Lc，tThermal load Lh，tAnd photovoltaic output Ppv，tData, t 1, 2 … 24;
step 2: number N of electric vehicles of which input systems need to be simulatedevAssuming that the traveling time and the returning time of the electric automobile meet normal distribution, the traveling time t of each electric automobile is obtained by using the probability distribution curve of the traveling time of the electric automobileinAnd a return time tout。
And step 3: the system obtains the travel OD matrix of the electric automobile through the Markov travel chain and the electric automobile transfer probability matrix.
And 4, step 4: inputting the vehicle data obtained in the steps 2 and 3 into a system, and calculating the shortest travel route y of each vehiclerAnd an on-the-way charging load
Figure BDA0003383984490000021
And 5: calculating the traffic at each momentThe calculation result is measured, and the number x of the running vehicles on each road in the traffic balance is countedaAnd the number x of charging vehicles per charging stationc；
Step 6: according to the road flow x in the traffic balanceaAnd charging station flow xcAnd guidance is provided for the traveling of the electric automobile at the subsequent moment.
And 7: circularly calculating the step 4-6 to obtain the traffic road flow x all dayaAnd an on-the-way charging load
Figure BDA0003383984490000023
And 8: inputting load data, generator output data and electric vehicle data obtained by simulation into a system, wherein the load data, the generator output data and the electric vehicle data comprise an electric load Le，tGas load Lg，tCold load Lc，tThermal load Lh，tPhotovoltaic output Ppv，tData and electric automobile trip path yrAnd a charging load in the process
Figure BDA0003383984490000022
Travel time tinAnd a return time toutRoad flow xaAnd the flow x of the charging stationcAnd determining constraint conditions of the optimization scheduling of the comprehensive energy system by taking the lowest operation cost of the system as an objective function, wherein the constraint conditions comprise load balance constraint, electric energy transmission power constraint, equipment climbing rate constraint, demand side response constraint, energy storage constraint and V2G control constraint:
min CIES＝Ce，b-Ce，s+Cgas+CDR (1)
wherein, Ce，bA cost for purchasing electricity from the grid; ce，sEarnings for selling electricity to the grid; cgasThe cost of gas is; cDRCompensating for the cost for the demand response; cIESThe operating cost of the comprehensive energy system;
the electricity purchase cost calculation formula is as follows:
Figure BDA0003383984490000031
wherein N is the number of each area in the garden; m is the number of scheduling time segments of the current day; ce，b，tThe price of the electricity purchased in the time period t; pe，b，i，tThe purchased power is t time period; delta t is the scheduling duration; ce，bThe cost for purchasing electricity;
the electricity selling income calculation formula is as follows:
Figure BDA0003383984490000032
wherein, Ce，s，tThe price of electricity sold in the time period t; pe，s，i，tSelling power for a period of t; delta t is the scheduling duration; m is the number of scheduling time segments of the current day; ce，sFor the benefit of selling electricity.
The gas charge calculation formula is as follows:
Figure BDA0003383984490000033
wherein, CgIs the unit heating value price of natural gas; pg，i，tIs the gas load;
the power balance constraints are as follows:
Pgridc，t-Pgridd，t+Ppv，t+Pgt，t＝Le，t+Pacc，t+Pach，t+Pec，t+Pbatc，t-Pbatd，t+Pevc，t-Pevd，t (5)
the upper and lower limits of output force are constrained as follows:
Figure BDA0003383984490000034
Figure BDA0003383984490000035
wherein,
Figure BDA0003383984490000036
and
Figure BDA0003383984490000037
exchanging upper and lower limits of power between the park comprehensive energy system and the power grid at the time t;
Figure BDA0003383984490000038
and
Figure BDA0003383984490000039
the upper and lower limits of the output of equipment i in the park comprehensive energy system at the time t;
the gas balance is constrained as follows:
Pg，t＝Lg，t+Lgt，t+Lgb，t (8)
wherein,
Figure BDA00033839844900000310
to know
Figure BDA00033839844900000311
The upper limit and the lower limit of the power exchanged between the comprehensive energy system and the power grid at the moment t are set;
the cold load balancing constraints are as follows:
Pec，t+Pacc，t＝Lc，t (9)
wherein L isc，tthe cooling load at time t; pec，tThe output power of the electric refrigerator; pacc，tThe refrigeration power of the air conditioner;
the thermal load balancing constraints are as follows:
αPgt，t+Pgb，t+Pach，t＝Lh，t (10)
wherein α is the thermoelectric ratio of the power output by the gas turbine; l ish，tIs the thermal load at time t; pgt，tGenerating heat power for the gas turbine; pach，tThe heating power of the air conditioner is obtained.
And step 9: and calculating based on the objective function and the constraint condition of the comprehensive energy system to obtain the optimal scheduling scheme of the comprehensive energy system considering traffic balance.
Further, the system establishes a demand side response strategy model based on the energy load characteristics of different users, transfers or reduces the heat load of the electricity, gas and heat loads in a time dimension through a demand side response strategy, and represents the load change to reduce the peak-valley difference of the system, wherein the demand side response strategy is obtained by adopting the following method:
step 1: in the comprehensive energy system, 3 loads of electricity, gas and heat have demand side response capacity, and the 3 loads can realize respective time dimension transfer, so that the 3 loads are divided into three parts which are respectively fixed loads
Figure BDA0003383984490000041
Transferable load
Figure BDA0003383984490000042
Can reduce the load
Figure BDA0003383984490000043
Namely:
Figure BDA0003383984490000044
wherein PL is the total load capacity;
Figure BDA0003383984490000045
is a fixed load;
Figure BDA0003383984490000046
is a transferable load;
Figure BDA0003383984490000047
to reduce the load;
step 2: transferable load
Figure BDA0003383984490000048
The time transfer can be carried out in the dispatching cycle, a transferable load model is established, and the load transfer quantity in the dispatching cycle can be calculated by substituting into the equations (12) and (13):
Figure BDA0003383984490000049
Figure BDA00033839844900000410
wherein,
Figure BDA00033839844900000411
is a sum of
Figure BDA00033839844900000412
Respectively representing the load quantities before and after the transferable load participates in the demand response and the load quantities participating in the demand response in the t period;
and step 3: can reduce the load
Figure BDA00033839844900000413
Different energy supply modes can be selected in the scheduling period to meet the load demand of the scheduling period, a reducible load model is established, and the reduction amount of each load in the scheduling period can be calculated in a formula (14):
Figure BDA00033839844900000414
wherein,
Figure BDA00033839844900000415
and
Figure BDA00033839844900000416
respectively representing the load quantity before and after the load participates in demand response and the load quantity participating in demand response in the t period;
and 4, step 4: obtaining the demand side in the scheduling strategy according to the steps 2 and 3The load amount of the response, and the compensation cost C of the system demand side response are calculateddr；
Figure BDA0003383984490000051
Wherein
Figure BDA0003383984490000052
The load quantity of the load participating in demand response can be reduced for the t period;
Figure BDA0003383984490000053
the load quantity of the load participating in the demand response can be transferred for the t period; alpha is a unit compensation coefficient of the transferable load; beta is a unit compensation coefficient of the reducible load; cdrThe cost of compensation for the system demand side response.
Further, the system establishes an electric vehicle travel model containing a charging load based on travel rules of electric vehicle users and traffic network road flow characteristics, simulates an actual travel route of the electric vehicle in a traffic network, generates a vehicle charging and discharging strategy, and realizes reduction of operation cost, wherein the electric vehicle travel model is obtained by adopting the following method:
step 1: inputting the number N of electric vehicles to be simulatedev；
Step 2: assuming that the traveling time of the electric automobile meets the probability distribution curve, generating the initial traveling time t of each electric automobile by using the formula (6)in：
Figure BDA0003383984490000054
In the formula (f)in(tin) Satisfying the probability distribution curve for the trip time; t is tinThe initial trip time of each electric automobile is; mu.sinAnd σinRespectively taking 9 and 1 as the expected value and the standard deviation of the travel time of the electric automobile;
and step 3: simulation of each electric vehicle using the Monte Carlo methodBattery SOC value SOC when automobile goes outEV(t), the travel route from the vehicle to a charging station can be ensured to be completed once, so that the travel requirement of the vehicle is met;
and 4, step 4: according to the travel OD pair of each vehicle, the shortest travel route of each vehicle in the transportation network is calculated, the electric vehicle can be charged according to the travel demand of the electric vehicle when running in the transportation network, so that the electric vehicle can reach a destination conveniently, and the objective function of the shortest route is that the travel time and the charging cost on the way are the least:
Figure BDA0003383984490000055
wherein,
Figure BDA0003383984490000056
the charging quantity of the electric vehicle at a charging station c is obtained; thetacCharging fee for charging station c;
Figure BDA0003383984490000057
charging fee for the way;
Figure BDA0003383984490000058
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA0003383984490000059
Is 1; laIs the road length;
Figure BDA00033839844900000510
cost for travel time;
Figure BDA00033839844900000511
the trip cost of the electric automobile is saved;
the shortest travel route model is as follows:
Δyr＝Dod (18)
Figure BDA00033839844900000512
Figure BDA00033839844900000513
Figure BDA00033839844900000514
wherein, Delta is a road incidence matrix; y isrSelecting a decision variable for a road of a vehicle; dodIs an OD pair matrix;
Figure BDA0003383984490000061
the battery SOC value of the vehicle at the node i;
Figure BDA0003383984490000062
the battery SOC value at node j for the vehicle; laIs the length of road a; w is the electricity consumption of the vehicle in hundred kilometers;
Figure BDA0003383984490000063
as an auxiliary variable, when the vehicle passes through the road a
Figure BDA0003383984490000064
Is 0; SOCEVminThe lowest SOC value of the battery when the vehicle runs is taken as the reference value; m is a particularly large constant;
Figure BDA0003383984490000065
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA0003383984490000066
Is 1;
and 5: when the vehicle reaches the destination, the vehicle starts to be charged, and the charging behavior of the electric vehicle is modeled as shown in the following formula:
Figure BDA0003383984490000067
SOCEV(tin)＝SOCEV，in (23)
SOCEV(tout)≥SOCEV，out (24)
0.2＜SOCEV(t)≤0.9 (25)
0≤PEV(t)≤P[tin，tout]EV，max (26)
therein, SOCEV(t) is the SOC value of the electric automobile at the time t; pEV(t) is the charging power of the electric automobile at the moment t; pEV，maxThe maximum charging power of the electric automobile; p [ t ]in，tout]EV，maxFor electric vehicles at tin，toutMaximum charging power in a time period; etaEVCharging efficiency for the electric vehicle; capEVThe battery capacity of the electric automobile; t is tin，toutRespectively showing the time when the electric automobile arrives at the charging pile and leaves the charging pile; SOCEV，outThe lowest electric quantity when the charging is finished; SOCEV，inIs the amount of electricity at the beginning of charging; Δ T is the scheduling time;
step 6: the vehicle can carry out V2G dispatch according to vehicle dwell time and owner's will when connecting charging pile and charging, nevertheless should guarantee that the SOC when leaving is not less than the SOC value when arriving, and can make electric automobile's initial SOC value reach the expectation SOC value when leaving in dwell time, promptly:
0≤SOCin≤SOCout≤1 (27)
Figure BDA0003383984490000068
Δtpatk＝tout-tin (29)
Δtpark≥3 (30)
therein, SOCinFor the quantity of electricity, SOC, at the beginning of charging of the vehicleoutThe quantity of electricity at the time of ending charging of the vehicle, PcatEV，maxAt maximum charging power, Δ tparkThe vehicle dwell time.
The vehicle with the electric automobile parking time exceeding 3 hours can carry out V2G dispatching so as to meet the charging requirement of the vehicle.
Further, the system is based on a traffic balance model, the travel route of the electric automobile during traffic balance is calculated by using the vehicle travel OD pairs, and traffic congestion is relieved, wherein the traffic balance model is obtained by adopting the following method:
step 1: the shortest travel route of the electric vehicle calculated according to the travel OD matrix in the step 3 in the claim 1 is counted to count the number x of the vehicles running on each road at the momentaAnd the number x of charging vehicles per charging stationc；
Step 2: for any one trip vehicle, when the trip costs on all active routes are equal and not greater than the trip costs on the inactive routes, the road flow distribution of the traffic network can reach a balanced state, namely a traffic balance state, and a traffic balance theory is modeled, namely:
Figure BDA0003383984490000071
Figure BDA0003383984490000072
Figure BDA0003383984490000073
Figure BDA0003383984490000074
Figure BDA0003383984490000075
wherein,
Figure BDA0003383984490000076
is od facing upwardThe flow rate of the path r; godTravel demand for od;
Figure BDA0003383984490000077
selecting a decision variable for the road of the path r;
Figure BDA0003383984490000078
a selection decision variable for charging station c; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
and step 3: after the road flow data after traffic balance at a certain moment is calculated, the road flow data is used as a reference value of traffic network flow at the next moment to guide vehicle traveling at the next moment, and an electric automobile traveling guide model is formed:
Figure BDA0003383984490000079
wherein, taIs road travel time; t is tcA time to charge the vehicle; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
Figure BDA00033839844900000710
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA00033839844900000711
Is 1;
Figure BDA00033839844900000712
the charging quantity of the electric vehicle at a charging station c is obtained; thetacIs the charge price;
Figure BDA00033839844900000713
the trip cost of the electric automobile is saved.
Further, the function is used for representing the relation between the vehicle running time and the road flow and the relation between the vehicle charging time and the charging station flow, and the electric vehicle running time and charging time model is obtained by adopting the following method:
the congestion effect of a road can be represented by a function of the vehicle travel time t and the traffic flowaAnd the traffic flow xaSatisfies the BPR function:
Figure BDA00033839844900000714
wherein, ta0Is the free passage time at road zero traffic flow; x is the number ofaIs the flow of road a; capaIs the capacity of road a; t is ta(xa) The traffic flow is xaRoad travel time of day.
Establishing a charging station waiting time model by referring to the BPR function, and obtaining the total charging time of the electric automobile at the c node of the charging station
Figure BDA0003383984490000081
Wherein, tc0Is the average waiting time of the charging station vehicle;
Figure BDA0003383984490000082
the charging quantity of the electric vehicle at a charging station c is obtained; pcIs the charging power;
Figure BDA0003383984490000083
charging station selects the variable 0-1, when the vehicle is charging at charging station c
Figure BDA0003383984490000084
Is 1; x is the number ofcThe flow rate for charging station c; capcCapacity of charging station c; t is tc(xc) For charging station flow xcTime of vehicle charging.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the invention takes a comprehensive energy system considering traffic balance as a research object, fully considers the space-time distribution characteristic of the charging load when the electric automobile runs in a traffic network, more accurately simulates the charging load of the electric automobile, realizes the flow optimization of the traffic network through the travel guidance of the electric automobile, reduces road congestion, provides a basis for the subsequent charging station investment construction,
the method excites the flexibility of user energy by introducing an electric automobile V2G technology and a demand side response strategy, realizes the reduction of system peak-valley difference, promotes the consumption of renewable energy, optimizes the use and conversion of various energy sources by ordered charging and discharging of the electric automobile and multi-energy coupling scheduling, and realizes the improvement of system operation economy. Meanwhile, the ordered guidance and demand side response strategy of the electric automobile can relieve the high carbon emission state of the thermal power generating unit and the gas generating unit, and the total carbon emission of the system is reduced.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flowchart of the overall steps of a method for optimal scheduling of an integrated energy system considering traffic balancing;
FIG. 2 is a time of use electricity price graph;
FIG. 3 is a structure diagram of a traffic network-grid-air network coupling net rack;
FIG. 4 is a bar graph of the amount of charge at each charging station as the vehicle travels;
FIG. 5 is a bar graph of traffic flow for each road when the vehicle is operating;
FIG. 6 is a histogram of the optimal scheduling results of the integrated energy system;
FIG. 7 is a bar chart showing charging and discharging states of a charging pile;
FIG. 8 is a graph of a condition in which load shedding participation in demand response may be achieved;
FIG. 8 is a graph of a condition in which load shedding participation in demand response may be achieved;
noun interpretation
OD (traffic volume): the OD survey is a survey of the start and stop points of traffic, which is also called OD traffic survey, and the OD traffic refers to the amount of traffic between the start and stop points. "O" is derived from ORIGIN, english, and refers to the starting point of a trip, and "D" is derived from DESTINATION, english, and refers to the DESTINATION of a trip.
SOC: the State of Charge, also called the remaining capacity, represents the ratio of the remaining dischargeable capacity to the capacity in its fully charged State, expressed as a percentage, after the battery has been used for a period of time or left unused for a long period of time.
V2G technique: the technology of the electric automobile for supplying power to the power grid is characterized in that a large number of energy storage sources of the electric automobile are used as the buffer of the power grid and renewable energy. The automobile-to-grid technology is receiving wide attention because the problems of grid inefficiency and renewable energy fluctuation can be relieved to a great extent through V2G, and benefits can be created for electric vehicle users.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of embodiments of the present application, generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
In the description of the embodiments of the present application, it should be noted that the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or orientations or positional relationships that the products of the present invention are usually placed in when used, and are only used for convenience of description and simplicity of description, but do not indicate or imply that the devices or elements that are referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present application. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
The present invention will be described in detail with reference to fig. 1 to 8.
The implementation method comprises the following steps:
1. the method comprises the following steps of (1) modeling electric automobile traveling and charging and discharging loads, considering the operation condition of a vehicle in a traffic network and the influence of V2G control, and modeling as follows:
assuming that the traveling time of the electric automobile meets the probability distribution curve, generating the initial traveling time t of each electric automobile by using the formula (6)in：
Figure BDA0003383984490000101
In the formula (f)in(tin) Satisfying the probability distribution curve for the trip time; t is tinThe initial trip time of each electric automobile is; mu.sinAnd σinRespectively is an expected value and a standard deviation of the electric automobile at the trip time;
simulating SOC value SOC of each electric automobile when the electric automobile travels by using Monte Carlo methodEV(t), the travel route from the vehicle to a charging station can be ensured to be completed once, so that the travel requirement of the vehicle is met;
according to the travel OD pair of each vehicle, the shortest travel route of each vehicle in the transportation network is calculated, the electric vehicle can be charged according to the travel demand of the electric vehicle when running in the transportation network, so that the electric vehicle can reach a destination conveniently, and the objective function of the shortest route is that the travel time and the charging cost on the way are the least:
Figure BDA0003383984490000102
wherein,
Figure BDA0003383984490000103
the charging quantity of the electric vehicle at a charging station c is obtained; thetacCharging fee for charging station c;
Figure BDA0003383984490000104
charging fee for the way;
Figure BDA0003383984490000105
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA0003383984490000106
Is 1; laIs the road length;
Figure BDA0003383984490000107
cost for travel time;
Figure BDA0003383984490000108
the trip cost of the electric automobile is saved;
constraint conditions are as follows:
(1.1) vehicle OD pair constraints
The road that the vehicle passes must have the nodes of the OD pairs as starting and ending points:
Δyr＝Dod (40)
wherein, Delta is a road incidence matrix; y isrSelecting a decision variable for a road of a vehicle; dodIs an OD pair matrix;
(1.2) SOC constraints during vehicle operation
The SOC value of the vehicle at each node continuously changes along with the driving process:
Figure BDA0003383984490000109
Figure BDA00033839844900001010
(1.3) vehicle minimum run SOC constraint
The vehicle cannot go below the minimum operating SOC value while running:
Figure BDA00033839844900001011
wherein,
Figure BDA00033839844900001012
the battery SOC value of the vehicle at the node i;
Figure BDA00033839844900001013
the battery SOC value at node j for the vehicle; laIs the length of road a; w is the electricity consumption of the vehicle in hundred kilometers;
Figure BDA00033839844900001014
as an auxiliary variable, when the vehicle passes through the road a
Figure BDA00033839844900001015
Is 0; SOCEVminThe lowest SOC value of the battery when the vehicle runs is taken as the reference value; m is a particularly large constant;
Figure BDA00033839844900001016
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA0003383984490000111
Is 1;
and when the vehicle reaches the destination, starting to charge the vehicle, and modeling the charging behavior of the electric automobile.
(1.4) vehicle charging constraints
In order to protect the vehicle battery, the SOC value of the vehicle during charging is set to be changed within the range of 0.2-0.9:
Figure BDA0003383984490000112
SOCEV(tin)＝SOCEV，in (45)
SOCEV(tout)≥SOCEV，out (46)
0.2＜SOCFV(t)≤0.9 (47)
0≤PEV(t)≤PEV，max t∈[tin，tout] (48)
therein, SOCEV(t) is the SOC value of the electric automobile at the time t; pEV(t) is the charging power of the electric automobile at the moment t; pEV，maxThe maximum charging power of the electric automobile; p [ t ]in，tout]EV，maxFor electric vehicles at tin，toutMaximum charging power in a time period; etaEVCharging efficiency for the electric vehicle; capEVThe battery capacity of the electric automobile; t is tin，toutRespectively showing the time when the electric automobile arrives at the charging pile and leaves the charging pile; SOCEV，outThe lowest electric quantity when the charging is finished; SOCEV，inIs the amount of electricity at the beginning of charging; Δ T is the scheduling time;
the vehicle can carry out V2G dispatch according to vehicle dwell time and owner's will when connecting charging pile and charging, but the SOC when guaranteeing to leave is not less than the SOC value when arriving, and can make electric automobile's initial SOC value reach the expectation SOC value when leaving in dwell time.
(1.5) vehicle V2G control constraints
The SOC of the vehicle when leaving is not less than the SOC value when arriving, and the vehicle with the parking time exceeding 3 hours can be subjected to the V2G scheduling so as to meet the charging requirement of the vehicle:
0≤SOCin≤SOCout≤1 (49)
Figure BDA0003383984490000113
Δtpark＝tout-tin (51)
Δtpark≥3 (52)
therein, SOCinFor the quantity of electricity, SOC, at the beginning of charging of the vehicleoutThe quantity of electricity at the time of ending charging of the vehicle, PcatEV，maxAt maximum charging power, Δ tparkFor stopping the vehicleAnd (4) remaining for a while.
As shown in fig. 2 and fig. 7 (see fig. 2 and fig. 7 of other documents for identification), the electric vehicle can output electric power at the time of peak price of electricity and charge at the time of valley price of electricity by using the V2G technology, thereby alleviating the amount of electricity purchased by the system and promoting the consumption of new energy, and reducing the operating cost of the system.
2. The electric automobile traffic balance trip modeling considers the schedulability of flow in a traffic network and the road congestion condition, and the model is as follows:
according to the calculated shortest travel path of the electric automobile, counting the number x of vehicles running on each road at the momentaAnd the number x of charging vehicles per charging stationc；
For any one trip vehicle, when the trip costs on all active routes are equal and not greater than the trip costs on the inactive routes, the road flow distribution of the traffic network can reach a balanced state, namely a traffic balance state, and a traffic balance theory is modeled, namely:
Figure BDA0003383984490000121
constraint conditions are as follows:
(2.1) travel demand constraint:
the total number of routes selected by the vehicle is equal to the vehicle travel demand:
Figure BDA0003383984490000122
(2.2) path traffic constraint:
the path traffic cannot be negative:
Figure BDA0003383984490000123
(2.3) road flow constraint:
Figure BDA0003383984490000124
(2.4) charging station flow restriction:
Figure BDA0003383984490000125
wherein,
Figure BDA0003383984490000126
the flow rate of od to the upper path r; godTravel demand for od;
Figure BDA0003383984490000127
selecting a decision variable for the road of the path r;
Figure BDA0003383984490000128
a selection decision variable for charging station c; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
after the road flow data after traffic balance at a certain moment is calculated, the road flow data is used as a reference value of traffic network flow at the next moment to guide vehicle traveling at the next moment, and an electric automobile traveling guide model is formed:
Figure BDA0003383984490000131
wherein, taIs road travel time; t is tcA time to charge the vehicle; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
Figure BDA0003383984490000132
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure BDA0003383984490000133
Is 1;
Figure BDA0003383984490000134
the charging quantity of the electric vehicle at a charging station c is obtained; thetacIs the charge price;
Figure BDA0003383984490000135
the trip cost of the electric automobile is saved.
As shown in fig. 4 and fig. 5 (specifically, see fig. 4 and fig. 5 of other documents), the travel route of the electric vehicle in the traffic network is guided at some time, and it can be seen that the traffic balance model has a more balanced traffic distribution than the road vehicle flow distribution of the shortest path model, so that the congestion of the traffic network can be alleviated, and the travel cost of the user can be reduced. Meanwhile, as the electric automobile selects a far path with less traffic flow in order to avoid congestion under a traffic balance model, the charging amount of some charging stations is increased.
3. The electric automobile driving time and the on-way charging time are characterized by using a function, and the model of the electric automobile driving time and the on-way charging time is as follows by taking the road capacity and the charging waiting time into consideration:
the congestion effect of a road can be represented by a function of the vehicle travel time t and the traffic flowaAnd the traffic flow xaSatisfies the BPR function:
Figure BDA0003383984490000136
wherein, ta0Is the free passage time at road zero traffic flow; x is the number ofaIs the flow of road a; capaIs the capacity of road a; t is ta(xa) The traffic flow is xaRoad travel time of day.
Establishing a charging station waiting time model by referring to the BPR function, and obtaining the total charging time t of the electric automobile at the charging station c nodes：
Figure BDA00033839844900001317
Wherein, tc0Is the average waiting time of the charging station vehicle;
Figure BDA0003383984490000137
the charging quantity of the electric vehicle at a charging station c is obtained; pcIs the charging power;
Figure BDA0003383984490000138
charging station selects the variable 0-1, when the vehicle is charging at charging station c
Figure BDA0003383984490000139
Is 1; x is the number ofcThe flow rate for charging station c; capcCapacity of charging station c; t is tc(xc) For charging station flow xcTime of vehicle charging.
4. The demand side response modeling considers flexible transfer and reducible property of the multi-element flexible load in time, and the model is as follows:
in the comprehensive energy system, 3 loads of electricity, gas and heat have demand side response capacity, and the 3 loads can realize respective time dimension transfer, so that the 3 loads are divided into three parts which are respectively fixed loads
Figure BDA00033839844900001310
Transferable load
Figure BDA00033839844900001311
Can reduce the load
Figure BDA00033839844900001312
Namely:
Figure BDA00033839844900001313
wherein, PLIs the total load capacity;
Figure BDA00033839844900001314
is a fixed load;
Figure BDA00033839844900001315
is transferable negativeLoading;
Figure BDA00033839844900001316
to reduce the load;
transferable load
Figure BDA0003383984490000141
The time transfer can be carried out in the scheduling period, and a transferable load model is established.
Constraint conditions are as follows:
(4.1) transferable load Power Balancing constraints
The load amount after the demand response should be the initial load amount plus the transfer load amount:
Figure BDA0003383984490000142
(4.2) Total load restraint
The total amount of transferable loads within a scheduling period is unchanged:
Figure BDA0003383984490000143
wherein,
Figure BDA0003383984490000144
is a sum of
Figure BDA0003383984490000145
Respectively representing the load quantities before and after the transferable load participates in the demand response and the load quantities participating in the demand response in the t period;
can reduce the load
Figure BDA0003383984490000146
Different energy supply modes can be selected in the scheduling period to meet the load requirements of the user, and a reducible load model is established.
(4.3) load power balance constraint can be reduced
The load value after the demand response is the initial load value plus the reduction load:
Figure BDA0003383984490000147
wherein,
Figure BDA0003383984490000148
and
Figure BDA0003383984490000149
respectively representing the load quantity before and after the load participates in demand response and the load quantity participating in demand response in the t period;
calculating the compensation cost C of the demand side response of the system according to the load quantity of the demand side response in the scheduling strategydr。
Figure BDA00033839844900001410
Wherein
Figure BDA00033839844900001411
The load quantity of the load participating in demand response can be reduced for the t period;
Figure BDA00033839844900001412
the load quantity of the load participating in the demand response can be transferred for the t period; alpha is a unit compensation coefficient of the transferable load; beta is a unit compensation coefficient of the reducible load; cdrThe cost of compensation for the system demand side response.
5. Determining the constraint condition of the optimized scheduling of the comprehensive energy system by taking the lowest operation cost of the system as an objective function:
min CIES＝Ce，b-Ce，s+Cgas+CDR (66)
wherein, Ce，bA cost for purchasing electricity from the grid; ce，sEarnings for selling electricity to the grid; cgasThe cost of gas is; cDRCompensating for the cost for the demand response; cIESAs a comprehensive energy sourceThe operating cost of the system;
the electricity purchase cost calculation formula is as follows:
Figure BDA0003383984490000151
wherein N is the number of each area in the garden; m is the number of scheduling time segments of the current day; ce，b，tThe price of the electricity purchased in the time period t; pe，b，i，tThe purchased power is t time period; delta t is the scheduling duration; ce，bThe cost for purchasing electricity;
the electricity selling income calculation formula is as follows:
Figure BDA0003383984490000152
wherein, Ce，s，tThe price of electricity sold in the time period t; pe，s，i，tSelling power for a period of t; delta t is the scheduling duration; m is the number of scheduling time segments of the current day; ce，sFor the benefit of selling electricity.
The gas charge calculation formula is as follows:
Figure BDA0003383984490000153
wherein, CgIs the unit heating value price of natural gas; pg，i，tIs the gas load;
constraint conditions are as follows:
(5.1) Power balance constraints
In scheduling, the power grid needs to satisfy power balance, that is, the power supply amount at each time is equal to the total load amount:
Pgridc，t-Pgridd，t+Ppv，t+Pgt，t＝Le，t+Pacc，t+Pach，t+Pec，t+Pbatc，t-Pbatd，t+Pevc，t-Pevd，t (70)
(5.2) Upper and lower limit constraints of output of equipment
The capacity of the equipment is limited, and the output of the equipment in unit time has upper and lower limits:
Figure BDA0003383984490000154
Figure BDA0003383984490000155
wherein,
Figure BDA0003383984490000156
and
Figure BDA0003383984490000157
exchanging upper and lower limits of power between the park comprehensive energy system and the power grid at the time t;
Figure BDA0003383984490000158
to know
Figure BDA0003383984490000159
The upper and lower limits of the output of equipment i in the park comprehensive energy system at the time t;
(5.3) gas load balance constraint
In scheduling, the air network needs to satisfy power balance, i.e. the air supply amount at each time is equal to the total load amount:
Pg，t＝Lg，t+Lgt，t+Lgb，t (73)
wherein,
Figure BDA0003383984490000161
and
Figure BDA0003383984490000162
the upper limit and the lower limit of the power exchanged between the comprehensive energy system and the power grid at the moment t are set;
(5.4) Cold load Balancing constraints
In scheduling, the cooling load needs to satisfy power balance, i.e. the amount of cooling supplied at each time is equal to the total load:
Pec，t+Pacc，t＝Lc，t (74)
wherein L isc，tthe cooling load at time t; pec，tThe output power of the electric refrigerator; pacc，tThe refrigeration power of the air conditioner;
(5.5) thermal load balance constraints
In scheduling, the thermal load needs to satisfy power balance, i.e. the heating load at each time is equal to the total load:
αPgt，t+Pgb，t+Pach，t＝Lh，t (75)
wherein α is the thermoelectric ratio of the power output by the gas turbine; l ish，tIs the thermal load at time t; pgt，tGenerating heat power for the gas turbine; pach，tThe heating power of the air conditioner is obtained.
The implementation steps are as follows:
the steps of the present invention are described below with reference to flow charts:
step 1: according to the historical load data, load data and generator output of each time interval of the next day of the system, such as the electric load L, are predicted by taking 24 hours as a scheduling intervale，tGas load Lg，tCold load Lc，tThermal load Lh，tAnd photovoltaic output Ppv，tData, t 1, 2 … 24;
step 2: number N of electric vehicles of which input systems need to be simulatedevThe traveling time and the returning time of the electric automobile both meet normal distribution, and the traveling time t of each electric automobile is obtained through the probability distribution curve of the traveling time of the electric automobileinAnd a return time tout；
And step 3: calculating through a Markov trip chain and an electric vehicle transfer probability matrix to obtain a trip OD matrix of the electric vehicle;
and 4, step 4: inputting the vehicle data obtained in the steps 2 and 3 into a system, and calculating the shortest travel route y of each electric vehiclerAnd an on-the-way charging load
Figure BDA0003383984490000163
And 5: calculating the traffic balance calculation result at each moment, and counting the number x of vehicles running on each road during traffic balanceaAnd the number x of charging vehicles per charging stationc；
Step 6: according to the road flow x in the traffic balanceaAnd charging station flow xcProviding guidance for the electric automobile to go out at the subsequent moment;
and 7: circularly calculating the step 4-6 to obtain the traffic road flow x all dayaAnd an on-the-way charging load
Figure BDA0003383984490000171
And 8: will simulate the resulting electrical load Le，tGas load Lg，tCold load Lc，tThermal load Lh，tPhotovoltaic output Ppv，tData and electric automobile trip path yrAnd a charging load in the process
Figure BDA0003383984490000172
Travel time tinAnd a return time toutRoad flow xaAnd the flow x of the charging stationcThe data input system takes the lowest operation cost of the system as an objective function, and determines constraint conditions of the optimization scheduling of the comprehensive energy system according to energy utilization requirements of flexibility of charging load of users of electric vehicles in a park, transferability of load of commercial users and reducibility of load of residential users, wherein the constraint conditions comprise load balance constraint, electric energy transmission power constraint, equipment climbing rate constraint, demand side response constraint, energy storage constraint and V2G control constraint:
min CIES＝Ce，b-Ce，s+Cgas+CDR (76)
wherein, Ce，bA cost for purchasing electricity from the grid; ce，sEarnings for selling electricity to the grid; cgasThe cost of gas is; cDRCompensating for the cost for the demand response; cIESThe operating cost of the comprehensive energy system;
the electricity purchase cost calculation formula is as follows:
Figure BDA0003383984490000173
wherein N is the number of each area in the garden; m is the number of scheduling time segments of the current day; ce，b，tThe price of the electricity purchased in the time period t; pe，b，i，tThe purchased power is t time period; delta t is the scheduling duration; ce，bThe cost for purchasing electricity;
the electricity selling income calculation formula is as follows:
Figure BDA0003383984490000174
wherein, Ce，s，tThe price of electricity sold in the time period t; pe，s，i，tSelling power for a period of t; delta t is the scheduling duration; m is the number of scheduling time segments of the current day; ce，sFor the benefit of selling electricity.
The gas charge calculation formula is as follows:
Figure BDA0003383984490000175
wherein, CgIs the unit heating value price of natural gas; pg，i，tIs the gas load;
the power balance constraints are as follows:
Pgridc，t-Pgridd，t+Ppv，t+Pgt，t＝Le，t+Pacc，t+Pach，t+Pec，t+Pbatc，t-Pbatd，t+Pevc，t-Pevd，t(80)
the upper and lower limits of output force are constrained as follows:
Figure BDA0003383984490000181
Figure BDA0003383984490000182
wherein,
Figure BDA0003383984490000183
and
Figure BDA0003383984490000184
exchanging upper and lower limits of power between the park comprehensive energy system and the power grid at the time t;
Figure BDA0003383984490000185
and
Figure BDA0003383984490000186
the upper and lower limits of the output of equipment i in the park comprehensive energy system at the time t;
the gas balance is constrained as follows:
Pg，t＝Lg，t+Lgt，t+Lgb，t (83)
wherein,
Figure BDA0003383984490000187
and
Figure BDA0003383984490000188
the upper limit and the lower limit of the power exchanged between the comprehensive energy system and the power grid at the moment t are set;
the cold load balancing constraints are as follows:
Pec，t+Pacc，t＝Lc，t (84)
wherein L isc，tthe cooling load at time t; pec，tThe output power of the electric refrigerator; pacc，tThe refrigeration power of the air conditioner;
the thermal load balancing constraints are as follows:
αPgt，t+Pgb，t+Pach，t＝Lh，t (85)
wherein α is the thermoelectric ratio of the power output by the gas turbine; l ish，tIs the heat negative at the moment tLoading; pgt，tGenerating heat power for the gas turbine; pach，tThe heating power of the air conditioner is obtained.
And step 9: and calculating based on the objective function and the constraint condition of the comprehensive energy system to obtain the optimal scheduling scheme of the comprehensive energy system considering traffic balance.
Simulation verification:
firstly, parameters and conditions of a simulation test are as follows:
the simulation calculation example of the invention takes a regional comprehensive energy system as a research object, comprises 6 types of regions of residential areas, commercial areas, office areas, schools, subway stations and parking lots, adopts an IEEE9 node power distribution network, a 7-node natural gas network and a 20-node traffic network as examples for simulation, and a coupling network is shown in figure 3 (particularly, see figure 3 of other certification documents). The dispatching cycle is 24h, the time interval is 1h, the electricity price adopts time-of-use electricity price, the time-of-use electricity price is shown in figure 1, and the unit heat value price of the natural gas is 0.283 yuan/(kWh). The areas contain different equipment, wherein the commercial areas and the office areas are integrated energy systems, and the other areas are conventional energy systems. Residential quarter, commercial district, office area and parking area all are equipped with electric automobile and fill electric pile, are 1000, 500, 500 and 3000 respectively, and only the electric automobile in the parking area participates in V2G control when charging, and the electric automobile in other regions is charging in order. In the traffic network, the travel OD pairs of each vehicle are randomly extracted, the traffic network comprises 5 charging stations, the electric vehicles adopt a fast charging mode when adopting a method of charging on the way in the traffic network, and adopt a slow charging mode after reaching a destination, the vehicle-mounted battery capacity of each electric vehicle is 24 kW.h, and the maximum driving mileage is 400 km. This document aims at the lowest operating cost of the campus. The plant parameters for the campus are shown in table 1 below.
TABLE 1 park Equipment parameters
Figure BDA0003383984490000191
In order to verify the influence of traffic balance on the optimized scheduling of the comprehensive energy system, two scenes are set as the verification scenes of the text.
Scene 1: the shortest path model is adopted for vehicle traveling.
Scene 2: and a traffic balance model is adopted for vehicle traveling.
II, simulation test results:
(1) traffic balance results
The vehicle travel result at a certain moment is selected for display, and as can be seen from fig. 6 (see fig. 6 of other documentations specifically), in the shortest-circuit model, because the influence of other electric vehicles is not considered, all electric vehicles select the shortest path for travel, which results in that the traffic of some road vehicles is too large, and the traffic of some road vehicles is small, which is not in accordance with the actual situation, and is also unreasonable for the shortest-circuit model. In comparison, the traffic balance model considers the interaction influence between the path selection behavior and the road congestion phenomenon, so that the traffic flow distribution and the charging load distribution are balanced, and the actual trip cost of the user is low.
(2) Optimized scheduling result of comprehensive energy system
Taking an office area as an example, an optimized scheduling result of the regional integrated energy system is shown as shown in fig. 6. Because the electric automobile can discharge to the equipment in the garden and can sell electricity to the electric wire netting at the electricity price peak period, so can obtain the profit through selling electricity to the electric wire netting when the garden operation, the garden operation cost has obtained obvious reduction. Compared with the traditional energy system, although the energy supply cost of the natural gas is improved by 19.9%, the energy supply cost of the power system is reduced by 0.93%, and the total energy supply cost of the park is reduced by 4.11%. The simulation result shows that compared with the traditional scheduling mode, the optimal scheduling advantage of the comprehensive energy system is obvious, and greater economic benefit can be obtained. The system operating costs are shown in table 2 below:
TABLE 2 park operating expenses
Cost/dollar in operations 342825.340525
Cost/yuan for purchasing gas from gas network 61824.970909
Cost/dollar for purchasing electricity from the grid 301206.153811
Cost/dollar for selling electricity to the grid 21578.492528
Cost/dollar for demand response 1372.708333
(3) Demand side response results
The working period changes before and after the transferable load participates in the demand response are shown in the following table 3. The amount of load change before and after the load participation demand response can be reduced is shown in fig. 8 (see fig. 8 of other documents for details).
TABLE 3 residential area demand response situation
Electrical appliance Demand response pre-op period Post demand response work period
Electrical appliance
1 8，9，10，11，12，13 9，10，12，13，14，15
Electrical appliance 2 9 13
Electrical appliance 3 10，11，12 9，12，13
Electrical appliance 4 11，12，13 12，13，15
According to the management response result of the demand side of the residential area, most electrical equipment is willing to accept adjustment along with the change of the compensation of each time interval, and the response condition is better. The user can improve the stability of the operation of the park electric power system while obtaining the amount compensation in the residential area, and the peak-valley difference is reduced.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (7)

1. An optimal scheduling method of an integrated energy system considering traffic balance is characterized by comprising the following steps:
step 1: the park comprehensive energy system predicts load data and generator output data of each time period of the next day of the system in a set scheduling time interval according to historical load data;
step 2: input system needs simulationNumber of electric vehicles NevAnd obtaining the travel time t of each electric automobile through the probability distribution curve of the travel time of the electric automobilesinAnd a return time tout；
And step 3: the system obtains a travel OD matrix of the electric automobile through the calculation of a Markov travel chain and an electric automobile transfer probability matrix;
and 4, step 4: inputting the vehicle data obtained in the steps 2 and 3 into a system, and calculating the shortest travel route y of each electric vehiclerAnd an on-the-way charging load
Figure FDA0003383984480000013
And 5: calculating traffic balance data at each moment, and counting the number x of vehicles running on each road during traffic balance according to the traffic balanceaAnd the number x of charging vehicles per charging stationc；
Step 6: according to the road flow x in the traffic balanceaAnd charging station flow xcProviding guidance for the electric automobile to go out at the subsequent moment;
and 7: circularly calculating the step 4-6 to obtain the traffic road flow x all dayaAnd an on-the-way charging load
Figure FDA0003383984480000014
And 8: inputting load data, generator output data and electric vehicle data obtained by simulation into a system, taking the lowest operation cost of the system as an objective function, and determining constraint conditions of optimization scheduling of the comprehensive energy system according to energy utilization requirements of electric vehicle user charging load flexibility, commercial user load transferability and resident user load reducibility in a set area;
and step 9: the system carries out calculation based on the objective function and the constraint condition of the comprehensive energy system to obtain the optimal scheduling scheme of the comprehensive energy system considering traffic balance.
2. The method as claimed in claim 1, wherein the constraint conditions in step 8 include a cold load balance constraint, a heat load balance constraint, a gas balance constraint, an upper and lower output limit constraint, a power balance constraint, a power transmission power constraint, a device ramp rate constraint, a demand side response constraint, an energy storage constraint and a V2G control constraint.
3. The method for optimizing and scheduling the integrated energy system based on the consideration of traffic balance as claimed in claim 2, wherein the calculation process of the constraint condition comprises:
the operation cost calculation formula of the comprehensive energy system is as follows:
minCIES＝Ce,b-Ce,s+Cgas+CDR (1)；
wherein, Ce,bA cost for purchasing electricity from the grid; ce,sEarnings for selling electricity to the grid; cgasThe cost of gas is; cDRCompensating for the cost for the demand response; cIESThe operating cost of the comprehensive energy system;
the electricity purchase cost calculation formula is as follows:
Figure FDA0003383984480000021
wherein N is the number of each area in the garden; m is the number of scheduling time segments of the current day; ce,b,tThe price of the electricity purchased in the time period t; pe,b,i,tThe purchased power is t time period; delta t is the scheduling duration; ce,bThe cost for purchasing electricity;
the electricity selling income calculation formula is as follows:
Figure FDA0003383984480000022
wherein, Ce,s,tThe price of electricity sold in the time period t; pe,s,i,tSelling power for a period of t; delta t is the scheduling duration; m is the number of scheduling time segments of the current day; ce,sEarning for selling electricity;
the gas charge calculation formula is as follows:
Figure FDA0003383984480000023
wherein, CgIs the unit heating value price of natural gas; pg,i,tIs the gas load;
the power balance constraints are as follows:
Pgridc,t-Pgridd,t+Ppv,t+Pgt,t＝
Le,t+Pacc,t+Pach,t+Pec,t+Pbatc,t-Pbatd,t+Pevc,t-Pevd,t (5)；
the upper and lower limits of output force are constrained as follows:
Figure FDA0003383984480000024
Figure FDA0003383984480000025
wherein,
Figure FDA0003383984480000026
and
Figure FDA0003383984480000027
exchanging upper and lower limits of power between the park comprehensive energy system and the power grid at the time t;
Figure FDA0003383984480000028
and
Figure FDA0003383984480000029
the upper and lower limits of the output of equipment i in the park comprehensive energy system at the time t;
the gas balance is constrained as follows:
Pg,t＝Lg,t+Lgt,t+Lgb,t (8)；
wherein,
Figure FDA00033839844800000210
and
Figure FDA00033839844800000211
the upper limit and the lower limit of the power exchanged between the comprehensive energy system and the power grid at the moment t are set;
the cold load balancing constraints are as follows:
Pec,t+Pacc,t＝Lc,t (9)；
wherein L isc,tthe cooling load at time t; pec,tThe output power of the electric refrigerator; pacc,tThe refrigeration power of the air conditioner;
the thermal load balancing constraints are as follows:
αPgt,t+Pgb,t+Pach,t＝Lh,t (10)；
wherein α is the thermoelectric ratio of the power output by the gas turbine; l ish,tIs the thermal load at time t; pgt,tGenerating heat power for the gas turbine; pach,tThe heating power of the air conditioner is obtained.
4. The method as claimed in claim 1, wherein the system establishes a demand-side response strategy model based on energy demand for park electric vehicle user charging load flexibility, business user load transferability and residential user load reducibility, transfers or reduces electricity, gas and heat loads in a time dimension through a demand-side response strategy, and represents load changes to reduce system peak-valley difference, wherein the demand-side response strategy is obtained by the following method:
step 1: in the comprehensive energy system, 3 loads of electricity, gas and heat are considered to have demand side response capability, and the 3 loads can realize the transfer of respective time dimension, so that the 3 loads are divided into three parts, namelyFixed load
Figure FDA0003383984480000031
Transferable load
Figure FDA0003383984480000032
Can reduce the load
Figure FDA0003383984480000033
Namely:
Figure FDA0003383984480000034
wherein, PLIs the total load capacity;
Figure FDA0003383984480000035
is a fixed load;
Figure FDA0003383984480000036
is a transferable load;
Figure FDA0003383984480000037
to reduce the load;
step 2: transferable load
Figure FDA0003383984480000038
The time transfer can be carried out in the dispatching cycle, a transferable load model is established, and the load transfer quantity in the dispatching cycle can be calculated by substituting into the equations (12) and (13):
Figure FDA0003383984480000039
Figure FDA00033839844800000310
wherein,
Figure FDA00033839844800000311
is a sum of
Figure FDA00033839844800000312
Respectively representing the load quantities before and after the transferable load participates in the demand response and the load quantities participating in the demand response in the t period;
and step 3: can reduce the load
Figure FDA00033839844800000313
Different energy supply modes can be selected in the scheduling period to meet the load demand of the scheduling period, a reducible load model is established, and the reduction amount of each load in the scheduling period can be calculated in a formula (14):
Figure FDA00033839844800000314
wherein,
Figure FDA00033839844800000315
and
Figure FDA00033839844800000316
respectively representing the load quantity before and after the load participates in demand response and the load quantity participating in demand response in the t period;
and 4, step 4: obtaining the load quantity of the demand side response in the scheduling strategy according to the steps 2 and 3, and calculating the compensation cost C of the demand side response of the systemdr；
Figure FDA0003383984480000041
Wherein
Figure FDA0003383984480000042
Reducing load participation requirements for a time period tSolving the load quantity of the response;
Figure FDA0003383984480000043
the load quantity of the load participating in the demand response can be transferred for the t period; alpha is a unit compensation coefficient of the transferable load; beta is a unit compensation coefficient of the reducible load; cdrThe cost of compensation for the system demand side response.
5. The optimal scheduling method of the integrated energy system considering traffic balance according to claim 1, wherein the system establishes an electric vehicle travel model containing a charging load based on travel rules of electric vehicle users and traffic network road flow characteristics, simulates an actual travel route of an electric vehicle in a traffic network, generates a vehicle charging and discharging strategy, and achieves reduction of travel cost, and the electric vehicle travel model is obtained by adopting the following method:
step 1: inputting the number N of electric vehicles to be simulatedev；
Step 2: assuming that the traveling time of the electric automobile meets the probability distribution curve, generating the initial traveling time t of each electric automobile by using the formula (6)in：
Figure FDA0003383984480000044
In the formula (f)in(tin) Satisfying the probability distribution curve for the trip time; t is tinThe initial trip time of each electric automobile is; mu.sinAnd σinRespectively is an expected value and a standard deviation of the electric automobile at the trip time;
and step 3: simulating SOC value SOC of each electric automobile when the electric automobile travels by using Monte Carlo methodEV(t), the travel route from the vehicle to a charging station can be ensured to be completed once, so that the travel requirement of the vehicle is met;
and 4, step 4: according to the travel OD pair of each vehicle, the shortest travel route of each vehicle in the transportation network is calculated, the electric vehicle can be charged according to the travel demand of the electric vehicle when running in the transportation network, so that the electric vehicle can reach a destination conveniently, and the objective function of the shortest route is that the travel time and the charging cost on the way are the least:
Figure FDA0003383984480000045
wherein,
Figure FDA0003383984480000046
the charging quantity of the electric vehicle at a charging station c is obtained; thetacCharging fee for charging station c;
Figure FDA0003383984480000047
charging fee for the way;
Figure FDA0003383984480000048
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure FDA0003383984480000049
Is 1; laIs the road length;
Figure FDA00033839844800000410
cost for travel time;
Figure FDA00033839844800000411
the trip cost of the electric automobile is saved;
the shortest travel route model is as follows:
Δyr＝Dod (18)；
Figure FDA00033839844800000412
Figure FDA00033839844800000413
Figure FDA00033839844800000414
wherein, Delta is a road incidence matrix; y isrSelecting a decision variable for a road of a vehicle; dodIs an OD pair matrix;
Figure FDA0003383984480000051
the battery SOC value of the vehicle at the node i;
Figure FDA0003383984480000052
the battery SOC value at node j for the vehicle; laIs the length of road a; w is the electricity consumption of the vehicle in hundred kilometers;
Figure FDA0003383984480000053
as an auxiliary variable, when the vehicle passes through the road a
Figure FDA0003383984480000054
Is 0; SOCEVminThe lowest SOC value of the battery when the vehicle runs is taken as the reference value; m is a particularly large constant;
Figure FDA0003383984480000055
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure FDA0003383984480000056
Is 1;
and 5: when the vehicle reaches the destination, the vehicle starts to be charged, and the charging behavior of the electric vehicle is modeled as shown in the following formula:
Figure FDA0003383984480000057
SOCEV(tin)＝SOCEV,in (23)；
SOCEV(tout)≥SOCEV,out (24)；
0.2<SOCEV(t)≤0.9 (25)；
0≤PEV(t)≤P[tin,tout]EV,max (26)；
therein, SOCEV(t) is the SOC value of the electric automobile at the time t; pEV(t) is the charging power of the electric automobile at the moment t; pEV,maxThe maximum charging power of the electric automobile; p [ t ]in,tout]EV,maxFor electric vehicles at tin,toutMaximum charging power in a time period; etaEVCharging efficiency for the electric vehicle; capEVThe battery capacity of the electric automobile; t is tin,toutRespectively showing the time when the electric automobile arrives at the charging pile and leaves the charging pile; SOCEV,outThe lowest electric quantity when the charging is finished; SOCEV,inIs the amount of electricity at the beginning of charging; Δ T is the scheduling time;
step 6: the vehicle can carry out V2G dispatch according to vehicle dwell time and owner's will when connecting charging pile and charging, nevertheless should guarantee that the SOC when leaving is not less than the SOC value when arriving, and can make electric automobile's initial SOC value reach the expectation SOC value when leaving in dwell time, promptly:
0≤SOCin≤SOCout≤1 (27)；
Figure FDA0003383984480000058
Δtpark＝tout-tin (29)；
Δtpark≥3 (30)；
therein, SOCinFor the quantity of electricity, SOC, at the beginning of charging of the vehicleoutThe quantity of electricity at the time of ending charging of the vehicle, PcatEV,maxAt maximum charging power, Δ tparkThe vehicle dwell time.
6. The method for optimizing and scheduling the comprehensive energy system considering traffic balance according to claim 1, wherein the system is based on a traffic balance model, calculates a travel path of the electric vehicle during traffic balance by using a vehicle travel OD pair, and relieves traffic congestion, and the traffic balance model is obtained by adopting the following method:
step 1: the shortest travel route of the electric vehicle calculated according to the travel OD matrix in the step 3 in the claim 1 is counted to count the number x of the vehicles running on each road at the momentaAnd the number x of charging vehicles per charging stationc；
Step 2: for any one trip vehicle, when the trip costs on all active routes are equal and not greater than the trip costs on the inactive routes, the road flow distribution of the traffic network can reach a balanced state, namely a traffic balance state, and a traffic balance theory is modeled, namely:
Figure FDA0003383984480000061
Figure FDA0003383984480000062
Figure FDA0003383984480000063
Figure FDA0003383984480000064
Figure FDA0003383984480000065
wherein,
Figure FDA0003383984480000066
flow rate of od to upper path r；godTravel demand for od;
Figure FDA0003383984480000067
selecting a decision variable for the road of the path r;
Figure FDA0003383984480000068
a selection decision variable for charging station c; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
and step 3: after the road flow data after traffic balance at a certain moment is calculated, the road flow data is used as a reference value of traffic network flow at the next moment to guide vehicle traveling at the next moment, and an electric automobile traveling guide model is formed:
Figure FDA0003383984480000069
wherein, taIs road travel time; t is tcA time to charge the vehicle; x is the number ofaIs the flow of road a; x is the number ofcThe flow rate for charging station c;
Figure FDA00033839844800000610
the variable 0-1 is selected for the road, when the vehicle passes the road a
Figure FDA00033839844800000611
Is 1;
Figure FDA00033839844800000612
the charging quantity of the electric vehicle at a charging station c is obtained; thetacIs the charge price;
Figure FDA00033839844800000613
the trip cost of the electric automobile is saved.
7. The method for optimizing and scheduling the comprehensive energy system considering traffic balance as claimed in claim 1, wherein the system uses a function to represent the relationship between the vehicle travel time and the road flow, and between the vehicle charging time and the charging station flow, and the model of the vehicle travel time and the charging time is obtained by the following method:
the congestion effect of a road can be represented by a function of the vehicle travel time t and the traffic flowaAnd the traffic flow xaSatisfies the BPR function:
Figure FDA0003383984480000071
wherein, ta0Is the free passage time at road zero traffic flow; x is the number ofaIs the flow of road a; capaIs the capacity of road a; t is ta(xa) The traffic flow is xaRoad travel time of time;
establishing a charging station waiting time model by referring to the BPR function, and obtaining the total charging time t of the electric automobile at the charging station c nodes：
Figure FDA0003383984480000072
Wherein, tc0Is the average waiting time of the charging station vehicle;
Figure FDA0003383984480000073
the charging quantity of the electric vehicle at a charging station c is obtained; pcIs the charging power;
Figure FDA0003383984480000074
charging station selects the variable 0-1, when the vehicle is charging at charging station c
Figure FDA0003383984480000075
Is 1; x is the number ofcThe flow rate for charging station c; capcCapacity of charging station c; t is tc(xc) For chargingStation traffic is xcTime of vehicle charging.
CN202111443026.XA 2021-11-30 2021-11-30 Comprehensive energy system optimization scheduling method considering traffic balance Active CN114022046B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111443026.XA CN114022046B (en) 2021-11-30 2021-11-30 Comprehensive energy system optimization scheduling method considering traffic balance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111443026.XA CN114022046B (en) 2021-11-30 2021-11-30 Comprehensive energy system optimization scheduling method considering traffic balance

Publications (2)

Publication Number Publication Date
CN114022046A true CN114022046A (en) 2022-02-08
CN114022046B CN114022046B (en) 2023-04-18

Family

ID=80067099

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111443026.XA Active CN114022046B (en) 2021-11-30 2021-11-30 Comprehensive energy system optimization scheduling method considering traffic balance

Country Status (1)

Country Link
CN (1) CN114022046B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114722714A (en) * 2022-04-14 2022-07-08 湖北工业大学 Electric power-traffic coupling network expansion planning method considering traffic balance
CN115954919A (en) * 2023-01-12 2023-04-11 国网湖北省电力有限公司十堰供电公司 Micro-grid multi-objective optimization scheduling method considering mobile energy storage vehicle access
CN117436672A (en) * 2023-12-20 2024-01-23 国网湖北省电力有限公司经济技术研究院 Comprehensive energy operation method and system considering equivalent cycle life and temperature control load

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012162646A1 (en) * 2011-05-26 2012-11-29 Ice Energy, Inc. System and method for improving grid efficiency utilizing statistical distribution control
CN108596373A (en) * 2018-04-09 2018-09-28 燕山大学 A kind of electricity-traffic coupling network dynamic equilibrium method for solving
CN110504708A (en) * 2019-08-09 2019-11-26 国家电网有限公司 The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource
CN110533225A (en) * 2019-08-07 2019-12-03 华北电力大学 A kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming
CN113255135A (en) * 2021-05-28 2021-08-13 西安交通大学 Electric automobile rapid charging load simulation method based on traffic balance
US20210342958A1 (en) * 2020-04-30 2021-11-04 Uchicago Argonne, Llc Transactive framework for electric vehicle charging capacity distribution

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012162646A1 (en) * 2011-05-26 2012-11-29 Ice Energy, Inc. System and method for improving grid efficiency utilizing statistical distribution control
CN108596373A (en) * 2018-04-09 2018-09-28 燕山大学 A kind of electricity-traffic coupling network dynamic equilibrium method for solving
CN110533225A (en) * 2019-08-07 2019-12-03 华北电力大学 A kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming
CN110504708A (en) * 2019-08-09 2019-11-26 国家电网有限公司 The power distribution network multiple target collaborative planning method of meter and charging station and distributed generation resource
US20210342958A1 (en) * 2020-04-30 2021-11-04 Uchicago Argonne, Llc Transactive framework for electric vehicle charging capacity distribution
CN113255135A (en) * 2021-05-28 2021-08-13 西安交通大学 Electric automobile rapid charging load simulation method based on traffic balance

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114722714A (en) * 2022-04-14 2022-07-08 湖北工业大学 Electric power-traffic coupling network expansion planning method considering traffic balance
CN115954919A (en) * 2023-01-12 2023-04-11 国网湖北省电力有限公司十堰供电公司 Micro-grid multi-objective optimization scheduling method considering mobile energy storage vehicle access
CN117436672A (en) * 2023-12-20 2024-01-23 国网湖北省电力有限公司经济技术研究院 Comprehensive energy operation method and system considering equivalent cycle life and temperature control load
CN117436672B (en) * 2023-12-20 2024-03-12 国网湖北省电力有限公司经济技术研究院 Comprehensive energy operation method and system considering equivalent cycle life and temperature control load

Also Published As

Publication number Publication date
CN114022046B (en) 2023-04-18

Similar Documents

Publication Publication Date Title
Ghasemi-Marzbali Fast-charging station for electric vehicles, challenges and issues: A comprehensive review
CN110533225B (en) Business park comprehensive energy system optimal scheduling method based on opportunity constraint planning
CN114022046B (en) Comprehensive energy system optimization scheduling method considering traffic balance
CN112467722B (en) Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station
CN109492791B (en) Inter-city expressway network light storage charging station constant volume planning method based on charging guidance
CN111310966A (en) Micro-grid site selection and optimal configuration method containing electric vehicle charging station
Yu et al. A real time energy management for EV charging station integrated with local generations and energy storage system
CN112183882B (en) Intelligent charging station charging optimization method based on electric vehicle quick charging requirement
CN113987734A (en) Multi-objective optimization scheduling method for park comprehensive energy system under opportunity constraint condition
Singh et al. A real-time smart charging station for EVs designed for V2G scenario and its coordination with renewable energy sources
Yang et al. Coordination and optimization of CCHP microgrid group game based on the interaction of electric and thermal energy considering conditional value at risk
Kumar et al. Leveraging energy flexibilities for enhancing the cost-effectiveness and grid-responsiveness of net-zero-energy metro railway and station systems
Guo et al. Hierarchical game for low-carbon energy and transportation systems under dynamic hydrogen pricing
Xia et al. Optimal planning of photovoltaic-storage fast charging station considering electric vehicle charging demand response
He et al. Expansion planning of electric vehicle charging stations considering the benefits of peak‐regulation frequency modulation
CN113052450B (en) Urban energy Internet planning method suitable for electric energy substitution development strategy
Aktar et al. Scheduling of mobile charging stations with local renewable energy sources
Akhavan-Rezai et al. Priority-based charging coordination of plug-in electric vehicles in smart parking lots
Hasankhani et al. Optimal charge scheduling of electric vehicles in smart homes
Bai et al. Multi-objective planning for electric vehicle charging stations considering TOU price
Wang et al. Refined charging strategy for electric buses based on data-driven
CN114006390A (en) Electric vehicle charging load participation power grid interaction simulation method and system
Karandinou et al. A Method for the Assessment of Multi-objective Optimal Charging of Plug-in Electric Vehicles at Power System Level
Wang et al. An Optimal Scheduling Strategy for PhotovoltaicStorage-Charging Integrated Charging Stations
Qiu et al. Research on smart charging and discharging decision model of electric vehicle based on energy router

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
GR01 Patent grant
GR01 Patent grant