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EV Charging Coordination in Blockchain-Based Energy Markets
Mohammed, Mahdi Ali
Mohammed, Mahdi Ali
Date
2022-04
Author
Advisor
Type
Thesis
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Description
A Master of Science thesis in Electrical Engineering by Mahdi Ali Mohammed entitled, “EV Charging Coordination in Blockchain-Based Energy Markets”, submitted in April 2022. Thesis advisor is Dr. Mostafa Shaaban and thesis co-advisor is Dr. Mohammed Nassar. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
The deployment of distributed energy resources (DERs), including electric vehicles (EVs), is increasing, and is expected to be growing for the next decades. This direction opened the path for decentralized energy trading and resulted in a need for a secure and cost-effective energy trading platform. Blockchain-based peer-to-peer (P2P) energy markets provide a secure way for prosumers to profit off their DERs without reliance on a centralized utility or third-party. Thus, the adoption of such blockchains would create an abundance of information on the energy market and opens the door for market analysis. This thesis introduces a new framework to demonstrate the several opportunities brought to the operation of the power system side of electrified transportation by having the Blockchain layer. The proposed framework is based on the extraction and use of the publicly available information from a blockchain-based P2P energy market to produce price forecasts that are used to minimize charging costs in an EV charging lot, a business expected to become common with increasing EV market penetration. The proposed algorithm uses two resolutions for charging optimization by dividing the targeted charge into multiple dynamic state-of-charge (SOC) goals depending on rolling-window price forecasts. A linear approximation of the nonlinear EV charging characteristics is also utilized to significantly reduce computational load. Real parking lot data and multiple EV types and are considered to mimic real-life scenarios. Case studies are presented to illustrate the effectiveness of the algorithm and the results show a 16.12% reduction in overall charging costs, better than the closest counterpart by 4.5%. Moreover, the proposed algorithm’s runtimes never exceeded the 10-minute time limit and were less than 10% of the dynamic counterpart whose runtimes regularly exceeded the time limit when the number of EVs was the weekday normal. Since vehicle-to-grid (V2G) operation is also possible in such an energy market we consider a V2G case and simplify the problem to exclude binary variables. Results showed that in some cases many factors could obstruct V2G operation resulting in only 1% cost reduction over the grid-to-vehicle-only (G2V-only) case.