- Email: [email protected]
A Charging Strategy with the Price Stimulus Considering the Queue of Charging Station and EV Fast Charging Demand
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Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
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Energy (2018) 000–000 400–405 EnergyProcedia Procedia145 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, Applied Energy Symposium and Forum, Energy Integration REM 2017, 18–20 Renewable October 2017, Tianjin, China with Mini/Microgrids, REM 2017, 18–20 October 2017, Tianjin, China
A Charging Strategy with the Price Stimulus Considering the Queue 15th International Symposium on District Heating and Cooling A ChargingThe Strategy with the Price Stimulus Considering the Queue of Charging Station and EV Fast Charging Demand of Charging Station and EV Fast Charging Demand Assessing the feasibility of using the heat demand-outdoor Kai Yuanaa, Yi Songa,a,*, Yinchi Shaobb, Chongbo Sunaa, Zhili Wuaa Kai Yuan , Yi Song Shao , Chongbo Wu forecast temperature function for*,aYinchi long-term district Sun heat, Zhili demand State Grid Economic and Technological Research Institute Co. Ltd., Beijing 102209, China a
KeyaState Laboratory of Smart and GridTechnological of Ministry ofResearch Education, TianjinCo. University, Tianjin 300072, China Grid Economic Institute Ltd., Beijing 102209, China a,b,c a a b c b Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China b
I. Andrić a
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract This paper proposes an electric vehicle (EV) charging guiding strategy with the price stimulus considering the queue of charging This paper an charging electric vehicle (EV) charging guidingguiding strategy with the price considering the queueinteraction of charging station and proposes the EV fast demand. Firstly, a charging framework basedstimulus on the real-time information is station and the EV fastthe charging demand. Firstly, chargingisguiding framework based the real-time information interaction is introduced. Secondly, Origin-destination (OD)aanalysis introduced to describe theon distribution of EV fast charging demand. Abstract introduced. Secondly, Origin-destination (OD) analysis is introduced to describe the distribution EVcharging fast charging demand. Based on the chargingthe demand, a stack queue model is proposed to simulate the dynamic queue ofof EV stations, and a Basedstimulus on the charging queue model to charging simulate station the dynamic queue of EV charging cost. stations, and a price method isdemand, adoptedatostack guide the EV useristoproposed choose the for minimizing the charging Finally, District heating networks arethecommonly addressed in literature as onestation of the for most effective the solutions forcost. decreasing price method adopted to guide the EV userstrategy. to the choose the charging minimizing charging Finally,the a case isstimulus demonstrated toisprove effectiveness of the greenhouse gas emissions from building sector. systems require high investments which are returned through the heat case is demonstrated to prove the the effectiveness of the These strategy. sales. Due to theElsevier changed climate conditions and building renovation policies, heat demand in the future could decrease, Copyright © 2018 Ltd. All rights reserved. Copyright © 2018 The Authors. Published by Elsevier Ltd. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the Applied Energy Symposium and Forum, Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, The main scope of this paper is to assess the feasibility of using thecommittee heat demand – outdoor temperature function forand heatForum, demand Selection and peer-review under responsibility of the scientific of the Applied Energy Symposium Renewable Energy Integration with REM 2017. Renewable Energy Integration with Mini/Microgrids, Mini/Microgrids, REM 2017 forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Renewable Energy Integration with Mini/Microgrids, REM 2017. buildingsfast that vary charging in both load; construction periodcharging and typology. Threestrategy weather scenarios (low, medium, high) and three district Keywords: charge; dynamic queue; price; guiding renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: fast charge; charging load; dynamic queue; charging price; guiding strategy compared with results from a dynamic heat demand model, previously developed and validated by the authors. results showed that when only weather change is considered, the margin of error could be acceptable for some applications 1.The Introduction error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). As a new kind of energy vehicle, the electric vehicles (EVs) have great advantages and potentials in reducing the The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the As a new kindemissions of energy vehicle, the electric vehicles (EVs) have great advantages and potentials in of reducing the [1]. The charging demand of has distinctive spatialcarbon and alleviating energy crisis the decreasedioxide in the number of heating hours of 22-139h during heating season (depending on EVs the combination weather and carbon dioxide emissions and alleviating energy crisis [1]. The charging demand of EVs has distinctive spatialtemporal characteristics, which have significant influences selecting the charging stations. EV(depending charging mode renovation scenarios considered). On the other hand, function on intercept increased for 7.8-12.7% per The decade on the temporal characteristics, whichsuggested have on selecting the charging stations. The EV sites charging mode can be divided into fast and significant slow charging. slow charging power is lower, andthecharging is flexible coupled scenarios). The charging values could beinfluences usedThe to modify the function parameters for scenarios considered, and can be divided into fast charging and slow charging. The slow charging power is lower, and charging sites is flexible improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-010-66602386. Cooling. E-mail address:author. [email protected]. * Corresponding Tel.: +86-010-66602386.
E-mail address: [email protected]. Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum, Renewable Energy Integrationand with Mini/Microgrids, REM 2017. of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Selection peer-review under responsibility Integration with Mini/Microgrids, REM 2017. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2017 10.1016/j.egypro.2018.04.046
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Kai Yuan et al. / Energy Procedia 145 (2018) 400–405 Kai Yuan et al / Energy Procedia 00 (2018) 000–000
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(home, workplace, parking lot, etc.), while the fast charging usually requires centralized charging stations with a large charging power. The existing layout of fast charging stations and the distribution of EV charging demand are usually not matching. With the fast development of EVs, the restriction from the EV charging facilities becomes more and more serious. At the same time, the problems of unbalanced utilization of the stations, long queuing time of EV users and low voltage of the distribution network are also left to be solved [2]. Considering the coordinative potential among EVs, traffic network, charging stations and distribution network, an effective charging guide strategy, which can meet the changing requirements of EV users, is critical for the future adoption of EVs. And, the strategy should also promote the utilization of the charging stations and ensure the security of the distribution network. The current researches, on the one hand, focused on the forecasting method of EV charging load. Based on the travel habits of EV users, a forecast model to determine the spatial-temporal distribution of EV charging load was proposed in [3], which can reflect the actual distribution of EV charging load in urban areas. On the other hand, the researches related to the routing optimization and charging guidance were carried out. The concept of "electric distance" was put forward in [4], in which the framework of routing optimization is preliminarily established. Taken the behaviors of EV users into consideration, the game theory was introduced in [5] to guides users to actively participate in choosing the charging stations. Studies in [6] presented a hierarchical framework to coordinate the EV charging load considering the constraints from distribution network, which is an effective way to reduce the peak load as well as the operation costs. The flexible EV charging time and location were usually considered as the optimization parameters in the above researches to achieve the peak shaving and valley filling for power system, and reduce the charging cost. However, for the EVs with urgent charging demand, it is difficult to change these two parameters for system optimization. Therefore, it is of significant importance to develop new charging strategies for EV fast charging. Based on the distribution of charging demand, a charging guiding strategy is proposed in this paper considering the load acceptance capability and the dynamic queue of the EV charging stations. On the perspective of EV users, this strategy takes the minimum charging cost as the objective function to guide the EV users to choose the charging stations in an optimized way, which can also balance the utilization among the charging stations. Nomenclature T Ak/Vk Ck tw tc td p0 pd pc pk Pfk Pfk ,v Capf Capr
time set of events length of actual queue/virtual queue of charging station k number of charging piles of charging station k waiting time of the EV time of completing charging time of delivering charging request unit electricity price purchased from the grid unit time cost unit penalty price actual charging price of station k fast charging load of station k the threshold of fast charging load of charging station k battery level when charging is completed battery level when charging starts
2. Framework of the charging guiding strategy With the concepts of Intelligent Transportation System (ITS) [4] and Smart Charging Guide System (SCGS) [7], it is feasible to realize a real-time information interaction among different bodies. The framework of the proposed strategy is shown as Fig.1.
Kai Yuan et al. / Energy Procedia 145 (2018) 400–405 Kai Yuan et al / Energy Procedia 00 (2018) 000–000
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Data
Method
Model
Policy
Geographic data & road topology
OD analysis +
MERGE EV project Database
Monte Carlo + method
Floyd algorithm
Fast charging demand model
Charging station 1 Policy 1
Number of charging piles
+
Stack queue model
Charging station i Policy i
stack clock propulsion +
3
Day-ahead load Forecasting
OPF calculation Charging price stimulus model
Charging station j Policy j
Decision of users
Fig. 1. Framework of the charging guiding strategy
The framework of the proposed strategy is based on the geographic data and road topology, the MERGE project database [8], the number of charging piles in charging stations and the day-ahead load forecasting conducted by the distribution network operator. Monte Carlo method is utilized to simulate the uncertainties of the EV characteristics; the origin-destination (OD) analysis, together with the Floyd algorithm are used to simulate the mobility of EVs and describe the distribution of charging demand. A stack queue model is developed using the stack clock propulsion method in the discrete event system to simulate the dynamic queue of charging stations. Based on the load constraint delivered by the optimal power flow (OPF) calculation, a charging price stimulus model is used to help charging stations formulate price policies in different situations. Next, this paper will have a brief introduction about the OD analysis, the stack queue model and the charging price stimulus model to demonstrate the effectiveness of this guiding strategy. 3. Charging guiding strategy based on spatial-temporal fast charging demand 3.1. charging demand based on the OD analysis OD analysis is a widely used method for traffic simulation [9]. In this paper, the OD analysis is used to depict the trajectory of EVs. OD matrixes are the core of OD analysis, which are used to describe the travel characteristic (t ,t 1)
of EVs. In this paper, the OD matrix Bmm is composed of 24 sub-matrices describing the traffic flow between the traffic nodes, where t is the time period and m is the number of nodes in a certain traffic network. The OD (t ,t 1) matrix Bmm is transformed into the OD probability matrix by (1) to reflect the travel probability distribution for EVs in different time. m
cijt ,t 1 bijt ,t 1 / bijt ,t 1 , (1 i m)
(1)
j 1
bijt ,t 1 (1≤i≤m, 1≤j≤m) is the element in Bm(t,tm1) , indicating the number of EVs from initial node i to the node j t ,t 1 during the period from t to t+1.Therefore, the element cij represents the probability of the EVs head to the node j
from the initial node i during the period from t to t +i. With the Monte Carlo sampling, the initial position O and the start time ts are assigned to each EV, and then the destination d of this travel can be sampled with the OD probability matrix of ts. By Floyd algorithm, the path from O to d can be depicted. After completing the simulation of a travel, set the destination d as the new starting node O. By repeatedly calling OD probability matrixes of the corresponding time, the continuous travel routes of an EV can be described [10]. 3.2. double stack queue model A double stack queue model is proposed based on the stack clock propulsion of discrete event system to simulate
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the queuing process in the charging stations, as shown in Fig.2. The queue can be divided into 2 stacks. The first stack is the actual queue of charging stations, which includes EVs in both charging state and waiting state. When the number of the first stack in station j is larger than the number of charging pile Cj, Some of the EVs have to wait as the colored circle in purple. The second stack is a virtual queue, which represents the EVs that have booked the charging station but not arrived yet. N1>C1 M1
1
2
N1
2
Charging demand place Charging station place
EV in waiting state EV accepted guiding S1
1
...
1
2 Second stack (Virtual queue)
Nk
2
S7
S6
Nk>Ck
Mk
S2
Station 1
1
Station k
First stack （Actual queue）
Fig. 2. Stack queue model
S4
S3
S5
Fig. 3. Distribution of fast charging demand
TA={ta1, ta2,... } represents the time set of event "EV delivers charging request"; TB={tb1, tb2,... } represents the time set of event "EV arrives the stations"; TC={tc1, tc2,... } represents the time set of event "EV completes charging", and then the time set T for all events is given in (2).
T T TB TC {t1 , t2 ,..., tn } A
(2)
At any event time t in set T, Ik(t) is used to indicate whether an EV delivers request to station k; Rk(t) is used to indicate whether an EV arrives at station k; Lk(t) is used to indicate whether an EV leaves station k. Then the length of the actual queue Ak(ti) and the length of the virtual queue Vk(ti) at next event time ti+1 can be counted as (3) and (4), respectively.
Ak (ti 1 ) Ak (ti ) Rk (ti 1 ) Lk (ti 1 )
(3)
Vk (ti 1 ) Vk (ti ) I k (ti 1 ) Rk (ti 1 )
(4)
With the queue length and the information of EVs in stations, sort the actual queue in ascending order into the set h= {tc1，tc2，...}, according to the completed charging times tci. The waiting time tw of an EV for charging station k is calculated by (5), in which tb∈TB is the arrival time of the EV.
0, Ak (tb ) Ck tw q Ak (tb ) Ck 0 h(q 1) tb ,
(5)
3.3. Charging price stimulus method The network operator calculates the threshold of fast charging load ( Pk f,t,V ) of charging station k at time period of t under certain constraint of the distribution network. It is regarded as overload if the fast charging load exceeds the Pk f,t,V , and the charging station should pay a penalty fee to compensate the distribution network for the increased operating cost by adjusting the power output. It is assumed that the EV users always prefer to the nearest charging station. When the station is overload, the station raises the charging price to stimulate the EV users to choose other stations that have enough charging capacities. The response equation of fast charging price 0 is presented as (6) to reflect price fluctuation corresponding to the fast charging load Pk f,t and the threshold Pk f,t,V , in which pn means the normal charging price without the guiding strategy, and θ is the stimulus coefficient. As a result, the pk(t) represents the actual charging price of station k at time period of t in this stimulus method.
Kai Yuan et al. / Energy Procedia 145 (2018) 400–405 Kai Yuan et al / Energy Procedia 00 (2018) 000–000
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pn , Pk f,t Pk f,t,V pk (t ) Pk f,t,V pn 1 log f Pk ,t
5
(6)
, Pk f,t Pk f,t,V
The charging cost described in (7) for a EV user is composed of two parts (charging fee and time cost). Capf is the battery level when the charging process is completed. The second item is the time cost, which covers all the time from the time of delivering request td to the time of completing charging t c , and pd is the unit time cost, which is about 22.21 yuan/h in this paper.
c pk (t )(Cap f Capr ) pd (tc td )
(7)
The income for station k in a certain period can be described in (8), in which y is the number of users choosing station k, and z is the number of time period that is overload. ΔTx is the duration of each period corresponding to z. p0 is the unit electricity price purchased from the grid and pc is the unit penalty price. Fk
y
(Cap f
z
Capr ) ( pk (t ) p0 ) Tx ( Pk f,t Pk f,t,V ) pc
(8)
i 1 x 1
4. Simulation Result An urban road network with 29 nodes is used as a test case. There are 7 charging stations distributed in the network with the number of charging piles: C= [15 18 15 15 15 18 25]. 1000 EVs are demonstrated and the fast charging demand distribution is shown in Fig.3. Obviously, if all the EV users choose the nearest charging station, the distribution of charging load is unbalanced and station 7 (S7) faces the largest charging pressure. In this strategy, the EV users are guided to choose the charging station with the goal of minimizing the charging cost. In order to show the effect of the proposed guiding strategy, 2 scenarios are defined as follows. Scenario 1: all users choose the nearest station without the guiding strategy, and the charging price remains at pn; Scenario 2: all users participate in the guiding strategy and the charging price pk(t) varies as (6). The selection results are shown in Fig.4. In scenario 1, the charging requests in S2 and S7 will exceed its charging capacity, while the remaining capacity in other stations is not fully used. When the guiding strategy is taken into consideration, the number of charging requests in S7 is significantly reduced, and request for other charging stations has increased in varying degrees. The total charging request has been fully satisfied as the utilization of the EV charging stations is more balanced. 50
Number of EVs
40
Scenario 1 Scenario 2
30 20 10 0
1
2
3
4 5 Station Number
6
7
Fig. 4. Selection results comparison in two scenarios
Fig. 5. Total incomes of charging stations in two scenarios
Fig.5. illustrates the difference of fast charging load at S7 and the resulting difference in total income of all the EV charging stations. It can be seen that, S7 remains overload during 11 a.m. to 6 p.m. in scenario 1, thus the charging station S7 has to pay the penalty fee. With the increase of EVs that accept the proposed guiding strategy, more EVs which intended to charge at S7 have changed their routes to charge at other charging stations, for they can benefit
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Kai Yuan et al. / Energy Procedia 145 (2018) 400–405 Kai Yuan et al / Energy Procedia 00 (2018) 000–000
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from a lower charging cost, so the overloading time of S7 is obviously reduced. It can also be seen from the stacked histogram in Fig.5, the total income of charging stations also increases. The results show that the guiding strategy proposed in this paper, on the one hand, can decrease the overload period for specific charging stations with more charging load, and reduce the penalty fee. On the other hand, it can balance the utilization among different EV charging stations and promote charging income for the charging stations. 5. Conclusion In this paper, an EV charging guiding strategy with the price stimulus considering the queue of charging station and the EV fast charging demand is proposed, which not only considers the disitribution of fast charging demand, but also considers the dynamic state of charging station queue. By price stimulus, the utilization of the EV charging stations is more balanced. The test case also indicates that this strategy is profitable for both EV users and the charging stations. More details about the stimulus to EV users’ behaviors will be discussed in the future work. Acknowledgements This work was supported by the National Key Research and Development Program of China (2016YFB0900100) and the Science and Technology Project of State Grid Corporation of China “Research on planning technology and development strategy of distribution network oriented to power system integrated reformation area”. References [1] Doucette RT, McCulloch MD. Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions[J]. Applied Energy 2011, 88(7), p. 2315-23． [2] Clement-Nyns K, Haesen E, Driesen J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems, 2012, 25(1), p. 371-380. [3] Yunfei Mu, Jianzhong Wu, Nick Jenkins et al. A spatial-temporal model for grid impact analysis of plug-in electric vehicles[J]．Applied Energy, 2012, 94, p. 395–405. [4] Qinglai Guo, Yao Wang. Research on architecture of ITS based smart charging guide system[C]//General Meeting of the IEEE Power and Energy Society. San Diego, CA: IEEE, 2011, p. 1-5. [5] Islam S.Bayram, Michailidis G, Devetsikiotis M. Unsplittable load balancing in a network of charging stations under QoS guarantee[J]. IEEE Transactions on Smart Grid, 2015, 6(3). [6] Zhiwei Xu, Wencong Su, Zechun Hu et al. A hierarchical framework for coordinated charging of plug-in electric vehicles in China [J]. IEEE Transactions on Smart Grid, 2016, 7(1) , p. 428-438. [7] Jennifer Johnson, MAshrur Chowdhury, Yiming He et al. Utilizing real-time information transferring potentials to vehicles to improve the fast-charging process in electric vehicles [J]．Transportation Research Part C: Emerging Technologies, 2013, 26, p. 352–366. [8] EU Merge Project. Deliverable 2.1: Modelling electric storage devices for electric vehicles[R], Task, 2010. [9] Hu SR, Wang CM. Vehicle detector deployment strategies for the estimation of network Origin destination demands using partial link traffic counts, IEEE Trans Intell Transp Syst, 2008, 9(2) , p. 288–300. [10]Shao Yinchi, Mu Yunfei, Yu Xiaodan, et al. A spatial temporal charging load forecast and impact analysis method for distribution network using EVs-triffic-distribution model [J]. Proceedings of the CSEE, 2017, 37(18):5207-5218.
ScienceDirect ScienceDirect
Energy Procedia 00 (2018) 000–000 Available online www.sciencedirect.com Available online atatwww.sciencedirect.com Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy (2018) 000–000 400–405 EnergyProcedia Procedia145 00 (2017) www.elsevier.com/locate/procedia
Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, Applied Energy Symposium and Forum, Energy Integration REM 2017, 18–20 Renewable October 2017, Tianjin, China with Mini/Microgrids, REM 2017, 18–20 October 2017, Tianjin, China
A Charging Strategy with the Price Stimulus Considering the Queue 15th International Symposium on District Heating and Cooling A ChargingThe Strategy with the Price Stimulus Considering the Queue of Charging Station and EV Fast Charging Demand of Charging Station and EV Fast Charging Demand Assessing the feasibility of using the heat demand-outdoor Kai Yuanaa, Yi Songa,a,*, Yinchi Shaobb, Chongbo Sunaa, Zhili Wuaa Kai Yuan , Yi Song Shao , Chongbo Wu forecast temperature function for*,aYinchi long-term district Sun heat, Zhili demand State Grid Economic and Technological Research Institute Co. Ltd., Beijing 102209, China a
KeyaState Laboratory of Smart and GridTechnological of Ministry ofResearch Education, TianjinCo. University, Tianjin 300072, China Grid Economic Institute Ltd., Beijing 102209, China a,b,c a a b c b Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China b
I. Andrić a
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Correc
IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract This paper proposes an electric vehicle (EV) charging guiding strategy with the price stimulus considering the queue of charging This paper an charging electric vehicle (EV) charging guidingguiding strategy with the price considering the queueinteraction of charging station and proposes the EV fast demand. Firstly, a charging framework basedstimulus on the real-time information is station and the EV fastthe charging demand. Firstly, chargingisguiding framework based the real-time information interaction is introduced. Secondly, Origin-destination (OD)aanalysis introduced to describe theon distribution of EV fast charging demand. Abstract introduced. Secondly, Origin-destination (OD) analysis is introduced to describe the distribution EVcharging fast charging demand. Based on the chargingthe demand, a stack queue model is proposed to simulate the dynamic queue ofof EV stations, and a Basedstimulus on the charging queue model to charging simulate station the dynamic queue of EV charging cost. stations, and a price method isdemand, adoptedatostack guide the EV useristoproposed choose the for minimizing the charging Finally, District heating networks arethecommonly addressed in literature as onestation of the for most effective the solutions forcost. decreasing price method adopted to guide the EV userstrategy. to the choose the charging minimizing charging Finally,the a case isstimulus demonstrated toisprove effectiveness of the greenhouse gas emissions from building sector. systems require high investments which are returned through the heat case is demonstrated to prove the the effectiveness of the These strategy. sales. Due to theElsevier changed climate conditions and building renovation policies, heat demand in the future could decrease, Copyright © 2018 Ltd. All rights reserved. Copyright © 2018 The Authors. Published by Elsevier Ltd. prolonging the investment return period. Copyright © 2018 Elsevier Ltd. Allresponsibility rights reserved. Selection and peer-review under of the scientific committee of the Applied Energy Symposium and Forum, Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, The main scope of this paper is to assess the feasibility of using thecommittee heat demand – outdoor temperature function forand heatForum, demand Selection and peer-review under responsibility of the scientific of the Applied Energy Symposium Renewable Energy Integration with REM 2017. Renewable Energy Integration with Mini/Microgrids, Mini/Microgrids, REM 2017 forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Renewable Energy Integration with Mini/Microgrids, REM 2017. buildingsfast that vary charging in both load; construction periodcharging and typology. Threestrategy weather scenarios (low, medium, high) and three district Keywords: charge; dynamic queue; price; guiding renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were Keywords: fast charge; charging load; dynamic queue; charging price; guiding strategy compared with results from a dynamic heat demand model, previously developed and validated by the authors. results showed that when only weather change is considered, the margin of error could be acceptable for some applications 1.The Introduction error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). As a new kind of energy vehicle, the electric vehicles (EVs) have great advantages and potentials in reducing the The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the As a new kindemissions of energy vehicle, the electric vehicles (EVs) have great advantages and potentials in of reducing the [1]. The charging demand of has distinctive spatialcarbon and alleviating energy crisis the decreasedioxide in the number of heating hours of 22-139h during heating season (depending on EVs the combination weather and carbon dioxide emissions and alleviating energy crisis [1]. The charging demand of EVs has distinctive spatialtemporal characteristics, which have significant influences selecting the charging stations. EV(depending charging mode renovation scenarios considered). On the other hand, function on intercept increased for 7.8-12.7% per The decade on the temporal characteristics, whichsuggested have on selecting the charging stations. The EV sites charging mode can be divided into fast and significant slow charging. slow charging power is lower, andthecharging is flexible coupled scenarios). The charging values could beinfluences usedThe to modify the function parameters for scenarios considered, and can be divided into fast charging and slow charging. The slow charging power is lower, and charging sites is flexible improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and * Corresponding author. Tel.: +86-010-66602386. Cooling. E-mail address:author. [email protected]. * Corresponding Tel.: +86-010-66602386.
E-mail address: [email protected]. Keywords: Heat demand; Forecast; Climate change 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility the scientific 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the Applied Energy Symposium and Forum, Renewable Energy Integrationand with Mini/Microgrids, REM 2017. of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Selection peer-review under responsibility Integration with Mini/Microgrids, REM 2017. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 Copyright © 2018 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, Renewable Energy Integration with Mini/Microgrids, REM 2017 10.1016/j.egypro.2018.04.046
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(home, workplace, parking lot, etc.), while the fast charging usually requires centralized charging stations with a large charging power. The existing layout of fast charging stations and the distribution of EV charging demand are usually not matching. With the fast development of EVs, the restriction from the EV charging facilities becomes more and more serious. At the same time, the problems of unbalanced utilization of the stations, long queuing time of EV users and low voltage of the distribution network are also left to be solved [2]. Considering the coordinative potential among EVs, traffic network, charging stations and distribution network, an effective charging guide strategy, which can meet the changing requirements of EV users, is critical for the future adoption of EVs. And, the strategy should also promote the utilization of the charging stations and ensure the security of the distribution network. The current researches, on the one hand, focused on the forecasting method of EV charging load. Based on the travel habits of EV users, a forecast model to determine the spatial-temporal distribution of EV charging load was proposed in [3], which can reflect the actual distribution of EV charging load in urban areas. On the other hand, the researches related to the routing optimization and charging guidance were carried out. The concept of "electric distance" was put forward in [4], in which the framework of routing optimization is preliminarily established. Taken the behaviors of EV users into consideration, the game theory was introduced in [5] to guides users to actively participate in choosing the charging stations. Studies in [6] presented a hierarchical framework to coordinate the EV charging load considering the constraints from distribution network, which is an effective way to reduce the peak load as well as the operation costs. The flexible EV charging time and location were usually considered as the optimization parameters in the above researches to achieve the peak shaving and valley filling for power system, and reduce the charging cost. However, for the EVs with urgent charging demand, it is difficult to change these two parameters for system optimization. Therefore, it is of significant importance to develop new charging strategies for EV fast charging. Based on the distribution of charging demand, a charging guiding strategy is proposed in this paper considering the load acceptance capability and the dynamic queue of the EV charging stations. On the perspective of EV users, this strategy takes the minimum charging cost as the objective function to guide the EV users to choose the charging stations in an optimized way, which can also balance the utilization among the charging stations. Nomenclature T Ak/Vk Ck tw tc td p0 pd pc pk Pfk Pfk ,v Capf Capr
time set of events length of actual queue/virtual queue of charging station k number of charging piles of charging station k waiting time of the EV time of completing charging time of delivering charging request unit electricity price purchased from the grid unit time cost unit penalty price actual charging price of station k fast charging load of station k the threshold of fast charging load of charging station k battery level when charging is completed battery level when charging starts
2. Framework of the charging guiding strategy With the concepts of Intelligent Transportation System (ITS) [4] and Smart Charging Guide System (SCGS) [7], it is feasible to realize a real-time information interaction among different bodies. The framework of the proposed strategy is shown as Fig.1.
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Data
Method
Model
Policy
Geographic data & road topology
OD analysis +
MERGE EV project Database
Monte Carlo + method
Floyd algorithm
Fast charging demand model
Charging station 1 Policy 1
Number of charging piles
+
Stack queue model
Charging station i Policy i
stack clock propulsion +
3
Day-ahead load Forecasting
OPF calculation Charging price stimulus model
Charging station j Policy j
Decision of users
Fig. 1. Framework of the charging guiding strategy
The framework of the proposed strategy is based on the geographic data and road topology, the MERGE project database [8], the number of charging piles in charging stations and the day-ahead load forecasting conducted by the distribution network operator. Monte Carlo method is utilized to simulate the uncertainties of the EV characteristics; the origin-destination (OD) analysis, together with the Floyd algorithm are used to simulate the mobility of EVs and describe the distribution of charging demand. A stack queue model is developed using the stack clock propulsion method in the discrete event system to simulate the dynamic queue of charging stations. Based on the load constraint delivered by the optimal power flow (OPF) calculation, a charging price stimulus model is used to help charging stations formulate price policies in different situations. Next, this paper will have a brief introduction about the OD analysis, the stack queue model and the charging price stimulus model to demonstrate the effectiveness of this guiding strategy. 3. Charging guiding strategy based on spatial-temporal fast charging demand 3.1. charging demand based on the OD analysis OD analysis is a widely used method for traffic simulation [9]. In this paper, the OD analysis is used to depict the trajectory of EVs. OD matrixes are the core of OD analysis, which are used to describe the travel characteristic (t ,t 1)
of EVs. In this paper, the OD matrix Bmm is composed of 24 sub-matrices describing the traffic flow between the traffic nodes, where t is the time period and m is the number of nodes in a certain traffic network. The OD (t ,t 1) matrix Bmm is transformed into the OD probability matrix by (1) to reflect the travel probability distribution for EVs in different time. m
cijt ,t 1 bijt ,t 1 / bijt ,t 1 , (1 i m)
(1)
j 1
bijt ,t 1 (1≤i≤m, 1≤j≤m) is the element in Bm(t,tm1) , indicating the number of EVs from initial node i to the node j t ,t 1 during the period from t to t+1.Therefore, the element cij represents the probability of the EVs head to the node j
from the initial node i during the period from t to t +i. With the Monte Carlo sampling, the initial position O and the start time ts are assigned to each EV, and then the destination d of this travel can be sampled with the OD probability matrix of ts. By Floyd algorithm, the path from O to d can be depicted. After completing the simulation of a travel, set the destination d as the new starting node O. By repeatedly calling OD probability matrixes of the corresponding time, the continuous travel routes of an EV can be described [10]. 3.2. double stack queue model A double stack queue model is proposed based on the stack clock propulsion of discrete event system to simulate
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the queuing process in the charging stations, as shown in Fig.2. The queue can be divided into 2 stacks. The first stack is the actual queue of charging stations, which includes EVs in both charging state and waiting state. When the number of the first stack in station j is larger than the number of charging pile Cj, Some of the EVs have to wait as the colored circle in purple. The second stack is a virtual queue, which represents the EVs that have booked the charging station but not arrived yet. N1>C1 M1
1
2
N1
2
Charging demand place Charging station place
EV in waiting state EV accepted guiding S1
1
...
1
2 Second stack (Virtual queue)
Nk
2
S7
S6
Nk>Ck
Mk
S2
Station 1
1
Station k
First stack （Actual queue）
Fig. 2. Stack queue model
S4
S3
S5
Fig. 3. Distribution of fast charging demand
TA={ta1, ta2,... } represents the time set of event "EV delivers charging request"; TB={tb1, tb2,... } represents the time set of event "EV arrives the stations"; TC={tc1, tc2,... } represents the time set of event "EV completes charging", and then the time set T for all events is given in (2).
T T TB TC {t1 , t2 ,..., tn } A
(2)
At any event time t in set T, Ik(t) is used to indicate whether an EV delivers request to station k; Rk(t) is used to indicate whether an EV arrives at station k; Lk(t) is used to indicate whether an EV leaves station k. Then the length of the actual queue Ak(ti) and the length of the virtual queue Vk(ti) at next event time ti+1 can be counted as (3) and (4), respectively.
Ak (ti 1 ) Ak (ti ) Rk (ti 1 ) Lk (ti 1 )
(3)
Vk (ti 1 ) Vk (ti ) I k (ti 1 ) Rk (ti 1 )
(4)
With the queue length and the information of EVs in stations, sort the actual queue in ascending order into the set h= {tc1，tc2，...}, according to the completed charging times tci. The waiting time tw of an EV for charging station k is calculated by (5), in which tb∈TB is the arrival time of the EV.
0, Ak (tb ) Ck tw q Ak (tb ) Ck 0 h(q 1) tb ,
(5)
3.3. Charging price stimulus method The network operator calculates the threshold of fast charging load ( Pk f,t,V ) of charging station k at time period of t under certain constraint of the distribution network. It is regarded as overload if the fast charging load exceeds the Pk f,t,V , and the charging station should pay a penalty fee to compensate the distribution network for the increased operating cost by adjusting the power output. It is assumed that the EV users always prefer to the nearest charging station. When the station is overload, the station raises the charging price to stimulate the EV users to choose other stations that have enough charging capacities. The response equation of fast charging price 0 is presented as (6) to reflect price fluctuation corresponding to the fast charging load Pk f,t and the threshold Pk f,t,V , in which pn means the normal charging price without the guiding strategy, and θ is the stimulus coefficient. As a result, the pk(t) represents the actual charging price of station k at time period of t in this stimulus method.
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pn , Pk f,t Pk f,t,V pk (t ) Pk f,t,V pn 1 log f Pk ,t
5
(6)
, Pk f,t Pk f,t,V
The charging cost described in (7) for a EV user is composed of two parts (charging fee and time cost). Capf is the battery level when the charging process is completed. The second item is the time cost, which covers all the time from the time of delivering request td to the time of completing charging t c , and pd is the unit time cost, which is about 22.21 yuan/h in this paper.
c pk (t )(Cap f Capr ) pd (tc td )
(7)
The income for station k in a certain period can be described in (8), in which y is the number of users choosing station k, and z is the number of time period that is overload. ΔTx is the duration of each period corresponding to z. p0 is the unit electricity price purchased from the grid and pc is the unit penalty price. Fk
y
(Cap f
z
Capr ) ( pk (t ) p0 ) Tx ( Pk f,t Pk f,t,V ) pc
(8)
i 1 x 1
4. Simulation Result An urban road network with 29 nodes is used as a test case. There are 7 charging stations distributed in the network with the number of charging piles: C= [15 18 15 15 15 18 25]. 1000 EVs are demonstrated and the fast charging demand distribution is shown in Fig.3. Obviously, if all the EV users choose the nearest charging station, the distribution of charging load is unbalanced and station 7 (S7) faces the largest charging pressure. In this strategy, the EV users are guided to choose the charging station with the goal of minimizing the charging cost. In order to show the effect of the proposed guiding strategy, 2 scenarios are defined as follows. Scenario 1: all users choose the nearest station without the guiding strategy, and the charging price remains at pn; Scenario 2: all users participate in the guiding strategy and the charging price pk(t) varies as (6). The selection results are shown in Fig.4. In scenario 1, the charging requests in S2 and S7 will exceed its charging capacity, while the remaining capacity in other stations is not fully used. When the guiding strategy is taken into consideration, the number of charging requests in S7 is significantly reduced, and request for other charging stations has increased in varying degrees. The total charging request has been fully satisfied as the utilization of the EV charging stations is more balanced. 50
Number of EVs
40
Scenario 1 Scenario 2
30 20 10 0
1
2
3
4 5 Station Number
6
7
Fig. 4. Selection results comparison in two scenarios
Fig. 5. Total incomes of charging stations in two scenarios
Fig.5. illustrates the difference of fast charging load at S7 and the resulting difference in total income of all the EV charging stations. It can be seen that, S7 remains overload during 11 a.m. to 6 p.m. in scenario 1, thus the charging station S7 has to pay the penalty fee. With the increase of EVs that accept the proposed guiding strategy, more EVs which intended to charge at S7 have changed their routes to charge at other charging stations, for they can benefit
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from a lower charging cost, so the overloading time of S7 is obviously reduced. It can also be seen from the stacked histogram in Fig.5, the total income of charging stations also increases. The results show that the guiding strategy proposed in this paper, on the one hand, can decrease the overload period for specific charging stations with more charging load, and reduce the penalty fee. On the other hand, it can balance the utilization among different EV charging stations and promote charging income for the charging stations. 5. Conclusion In this paper, an EV charging guiding strategy with the price stimulus considering the queue of charging station and the EV fast charging demand is proposed, which not only considers the disitribution of fast charging demand, but also considers the dynamic state of charging station queue. By price stimulus, the utilization of the EV charging stations is more balanced. The test case also indicates that this strategy is profitable for both EV users and the charging stations. More details about the stimulus to EV users’ behaviors will be discussed in the future work. Acknowledgements This work was supported by the National Key Research and Development Program of China (2016YFB0900100) and the Science and Technology Project of State Grid Corporation of China “Research on planning technology and development strategy of distribution network oriented to power system integrated reformation area”. References [1] Doucette RT, McCulloch MD. Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions[J]. Applied Energy 2011, 88(7), p. 2315-23． [2] Clement-Nyns K, Haesen E, Driesen J. The impact of charging plug-in hybrid electric vehicles on a residential distribution grid[J]. IEEE Transactions on Power Systems, 2012, 25(1), p. 371-380. [3] Yunfei Mu, Jianzhong Wu, Nick Jenkins et al. A spatial-temporal model for grid impact analysis of plug-in electric vehicles[J]．Applied Energy, 2012, 94, p. 395–405. [4] Qinglai Guo, Yao Wang. Research on architecture of ITS based smart charging guide system[C]//General Meeting of the IEEE Power and Energy Society. San Diego, CA: IEEE, 2011, p. 1-5. [5] Islam S.Bayram, Michailidis G, Devetsikiotis M. Unsplittable load balancing in a network of charging stations under QoS guarantee[J]. IEEE Transactions on Smart Grid, 2015, 6(3). [6] Zhiwei Xu, Wencong Su, Zechun Hu et al. A hierarchical framework for coordinated charging of plug-in electric vehicles in China [J]. IEEE Transactions on Smart Grid, 2016, 7(1) , p. 428-438. [7] Jennifer Johnson, MAshrur Chowdhury, Yiming He et al. Utilizing real-time information transferring potentials to vehicles to improve the fast-charging process in electric vehicles [J]．Transportation Research Part C: Emerging Technologies, 2013, 26, p. 352–366. [8] EU Merge Project. Deliverable 2.1: Modelling electric storage devices for electric vehicles[R], Task, 2010.