Open AccessIntegrated TOU price‐based demand response and dynamic grid‐to‐vehicle charge scheduling of electric vehicle aggregator to support grid stability
Author(s) -
Sharma Suman,
Jain Prerna
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12160
Subject(s) - news aggregator , demand response , grid , electric vehicle , computer science , scheduling (production processes) , real time computing , mathematical optimization , engineering , electrical engineering , electricity , mathematics , power (physics) , physics , geometry , quantum mechanics , operating system
Summary Unregulated and simultaneous charge scheduling of large‐scale electric vehicles (EVs) may lead to grid instability and peak demand rise. EV aggregator (EVA) can adopt coordinated and distributed charging rates for EVs over the connection hours providing stability and regulation to system operator (SO). However, against flat rate, price‐based demand response (PBDR) integrated scheduling motivates EVs to charge in off peak hours and utilize their flexibility for load levelling and peak reduction. PBDR reduces EV charging cost as well. Real‐time price (RTP) demand response (DR), mostly adopted in literature, has low acceptance rate by EVs for being too dynamic to response and being infeasible for few charge cycles in a day. Time of use (TOU), being less dynamic, is natural price signal for EVs under PBDR but may reduce EVA's profit. Considering this, an integrated TOU‐PBDR model for EVA's grid‐to‐vehicle (G2V) charge scheduling is proposed for energy and regulation market. EV owner perspective for charging cost minimization is considered along with EVA profit maximization. TOU is designed from RTP using agglomerative hierarchical clustering (AHC) method. EVs mobility uncertainty characterization makes scheduling dynamic. Sensitivity analysis based on charging rate upper limit and number of EVs validates the method. Results show interesting trends regarding EV charging cost, EVA profit, and regulation provision to SO.