Auc2Charge: An Online Auction Framework for Electric Vehicle - PowerPoint PPT Presentation

Auc2Charge: An Online Auction Framework for Electric Vehicle Park-and-Charge Qiao Xiang 1 , Fanxin Kong 1 , Xue Liu 1 , Xi Chen 1 , Linghe Kong 1 and Lei Rao 2 1 School of Computer Science, McGill University 2 General Motors Research Lab July 16th, 2015 Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 1/ 26

Introduction Electric Vehicles Introduction Electric Vehicles(EV) Crucial component of Intelligent Transportation System(ITS) Shift energy load from gasoline to electricity Cause high penetration of power grid Require large-scale deployment of charging stations Various charging stations Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 2/ 26

Introduction Park-and-Charge Park-and-Charge An up-and-coming mode for charging stations A parking lot equipped with Level 1 and Level 2 chargers EVs get charged during parking, e.g., a few hours Slow charging, inexpensive hardware and high utilization of space Controller A A Charging Points B B C C Parking Lot Figure: An illustration of park-and-charge Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 3/ 26

Introduction Park-and-Charge Current Field Deployment Workplace, airport, military base and etc. Pricing policies Pay-per-use Flat rate Boston University Seattle-Tacoma Airport Sources: bu.edu and plugincars.com Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 4/ 26

Motivation and Challenges Motivation Pay-Per-Use and Flat-Rate Pricing Advantages Simple and straightforward Helpful for early market expanding Limitations Overpricing and underpricing Undermined social welfare i.e., sum of station revenue and user utilities Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 5/ 26

Motivation and Challenges Motivation Social Welfare in Park-and-Charge: An Example Pay-per-use and flat-rate: allocate 15kWh to each EV Park and Charge A A SOC: 35/40 +15 SOC: 20/40 B B +15 SOC: 20/25 SOC: 5/25 However, Marginal utilities of EVs are different Lower arriving SOC → Higher marginal utility Ignorance of such difference → Undermined social welfare Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 6/ 26

Motivation and Challenges Motivation Social Welfare in Park-and-Charge: An Example To maximize social welfare: Allocate electricity to low SOC vehicle as much as possible Park and Charge A A SOC: 30/40 +10 SOC: 20/40 B B +20 SOC: 25/25 SOC: 5/25 Pay-per-use and flat-rate focus on station revenue, not social welfare. Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 7/ 26

Motivation and Challenges Motivation Motivation Future market deployment of park-and-charge desires an efficient market mechanism to Avoid overpricing and underpricing Maximize social welfare Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 8/ 26

Motivation and Challenges Our Focus Our Focus Our Focus Investigate auction as market mechanism for park-and-charge Auc2Charge : an online auction framework Understanding system benefits via numerical simulation Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 9/ 26

Motivation and Challenges Related Work Related Work Auctions has been widely studied in Internet Adwords, cloud computing and smart grid. Social welfare maximization Truthfulness and individual rationality What enables Auc2Charge ? Budget-constrained online auction and randomized auction theory Auc2Charge can be extended to other operation modes of charging stations, e.g., fast charging reservation. Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 10/ 26

System Settings and Problem Formulation System Settings and Problem Formulation ���������� � � ��������������� � � � � ����������� Bids AllocaKon ¡and ¡ Bids Pay ¡Decision A l l o P c SOC: ¡60% a a K y SOC: ¡30% ¡ o D n e ¡ Bid ¡1 Lose c a i Bid ¡1 Win s n i d o 2-­‑3pm, ¡$0.50, ¡5kWh ¡ ¡ ¡ n 2-­‑3pm, ¡$1.50, ¡6kWh ¡ ¡ Bid ¡2 Win Win Bid ¡2 3-­‑4pm, ¡$2.00, ¡9kWh . ¡. ¡. ¡. 3-­‑4pm, ¡$3.00, ¡8kWh . ¡. ¡. . ¡. ¡. EV ¡Customer ¡1 EV ¡Customer ¡N EVs arrive, park-and-charge, and leave Users send bids on how much to charge, when to charge and how much to pay, i.e., { b k j ( t ) , c k j ( t ) } , to the charging station Auctions are conducted every time slot, and users get notified Users can adjust future bids anytime during parking, Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 11/ 26

System Settings and Problem Formulation A Binary Programming Formulation T M K � � � b k j ( t ) y k Social Welfare PNC : maximize j ( t ) t =1 j =1 k =1 subject to K T � � b k j ( t ) y k Users Budget j ( t ) ≤ B j , ∀ j , t =1 k =1 M K � � c k j ( t ) y k Station Supply j ( t ) ≤ R ( t ) , ∀ t , j =1 k =1 K � y k No Double Wins j ( t ) ≤ 1 , ∀ j and t , k =1 K � c k j ( t ) y k Unit-Time Charging Capacity j ( t ) ≤ C j ( t ) , ∀ j and t , k =1 y k ∀ j , k and t . Winning Indication j ( t ) ∈ { 0 , 1 } , Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 12/ 26

System Settings and Problem Formulation Challenges Challenges PNC is NP-hard → The auction must be computationally efficient PNC is stochastic → The auction must be online Users may bid strategically → The autcion must be truthful and individual rational Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 13/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell Auc2Charge in a Nutshell 1. Decompose PNC into smaller auctions via bids update process. PNC one (1) PNC one (2) PNC Bids ¡Update ¡ Process PNC one (t) PNC one (T) Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 14/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell Auc2Charge in a Nutshell Bids Update Process : Originally proposed in budget-constrained online Adwords auction 1 , and extended to resource auction in cloud computing. 2 Intuition : adjust reported valuation in PNC one ( t ) based on the results from PNC one ( t − 1 ) Users not getting electricity in t − 1 → No adjust in t Users getting electricity in t − 1 → Reduce reported valuation in t based on remaining budget Rationale : avoid user depleting budget fast without fully charged Result : the overall budget constraint is dropped. 1Buchbinder, Niv, et al. ”Online primal-dual algorithms for maximizing ad-auctions revenue.” Algorithms-ESA 2007. 2Shi, Weijie, et al . ”An online auction framework for dynamic resource provisioning in cloud computing.” ACM SIGMETRICS 2014. Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 15/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell A Binary Programming Model without Budget Constraint M K � � ω k j ( t ) y k Social Welfare PNC one ( t ) : maximize p ( t ) = j ( t ) , j =1 k =1 subject to M K � � c k j ( t ) y k Station Supply j ( t ) ≤ R ( t ) , j =1 k =1 K � y k No Double Wins j ( t ) ≤ 1 , ∀ j k =1 K � c k j ( t ) y k Unit-Time Charging Capacity j ( t ) ≤ C j ( t ) , ∀ j k =1 y k ∀ j and k . Winning Indication j ( t ) ∈ { 0 , 1 } , Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 16/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell Auc2Charge in a Nutshell 2. Execute randomized auction for PNC one ( t ) PNC one (1) Auc one PNC one (2) Auc one PNC PNC one (t) Auc one PNC one (T) Auc one Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 17/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell Auc2Charge in a Nutshell Randomized Auction Auc one Basic idea : design truthful mechanism via approximation algorithm 3 1 Perform a fractional VCG auction for PNC one ( t ) 2 Decompose fractional solutions to PNC one ( t ) into a polynomial number of feasible solutions 3 Randomly select one feasible solution as the allocation decision 4 Compute the corresponding pricing decision 3Lavi, Ron, et al . ”Truthful and near-optimal mechanism design via linear programming.” Journal of the ACM (JACM) 58.6 (2011): 25. Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 18/ 26

Auc2Charge: An Online Auction Framework Auc2Charge in a Nutshell Auc2Charge in a Nutshell How to find a polynomial number of feasible solutions? Use a greedy primal-dual approximation algorithm for PNC one ( t ) as a separation oracle Greedy approximation algorithm Drop bids exceeding the unit-charging capacity Select the bid with highest unit-value, one at a time, while supply and demand lasts Theorem The greedy algorithm provides a close-form approximation ratio of α and an integrality gap of α to problem PNC one ( t ) in polynomial time. a a α = 1 + ǫ ( e − 1) θ θ − 1 . Qiao Xiang et al. (McGill) ACM e-Energy’15 07/16/2015 19/ 26