Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72459
Title: Flexible electric vehicle aggregation in distribution grids for participation in electricity markets
Authors: Recalde Melo, Dante Fernando
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Issue Date: 2017
Publisher: Nanyang Technological University
Source: Recalde Melo, D. F. (2017). Flexible electric vehicle aggregation in distribution grids for participation in electricity markets. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: Renewable energy generation and electric vehicles are viewed as promising technologies to lower emissions from conventional power plants and reduce the carbon footprint of the transportation sector. Liberalization of electricity markets provides possibilities for electric loads to bid their capacities in the energy and ancillary market. This work proposes an aggregation mechanism for flexible loads to obtain revenue by either direct participation in the demand response program or by bidding their capacities in the ancillary market through an aggregator. High penetration of electric vehicles arriving within a short time span and uncontrolled charging may require additional generation capacity; create congestion and in extreme cases result in interruption of supply. This work provides a framework for electric vehicle (EV) load aggregators to implement smart charging strategies that could help reduce the requirement for network reinforcement due to an increase in the demand. At the same time, the proposed method exploit the flexibility in the charging process for provision of ancillary services. Variability of renewable energy resources pose new challenges to current dispatch and operation methods used to balance demand and supply. Uncertainties inherent to these resources may affect the reliability of the system directly. This will require the power system operator to increase the amount of reserve and regulation procured, which increases the cost and reduces the efficiency. This work considers flexible load scheduling for local provision of ancillary services. This allows a higher share of renewable generation to be connected at the distribution level by ensuring any mismatch between the forecast and the actual energy output could be offset locally by the load aggregator. Flexibility provision by load aggregators depends directly on the willingness of end-users i.e. EV drivers, and building operators, to change their normal consumption patterns based on the system requirements. Due to the high investment cost and limited range, EV drivers may not be willing to participate in demand response programs unless the provided incentives can cover the increased battery degradation due to the provision of ancillary services. A compensation mechanism for providing monetary incentives to EV owners resulting from the increase in battery degradation is proposed. This method ensures that the EV charging schedule is not changed unless the new schedule results in economic benefits for the EV owner. Optimization of the distribution grid operation can only be accomplished if an equilibrium is reached for the objective of both system operators and load aggregators. Line limits and bus voltages should be kept within safe margins to prevent activation of the protective equipment. This work proposes a method to co-optimize the schedule for all aggregators which are connected to the same distribution grid and participate in the wholesale electricity market. An algorithm for obtaining the optimal network configuration resulting in minimization of the operating cost while considering voltage limits and preventing congestion in the lines is presented.
URI: http://hdl.handle.net/10356/72459
DOI: 10.32657/10356/72459
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: embargo_20220731
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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