University of Tasmania
Tavakoli_whole_thesis.pdf (3.98 MB)

Energy exchange between unidirectional vehicle-to-grid aggregators, and wind and conventional generating companies in the electricity market

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posted on 2023-05-27, 11:16 authored by Tavakoli, A
The future of humanity is dependent on saving the environment from global warming caused by CO2 emission from electricity generation and transportation systems. The remedies are the increasing in the penetration of renewable energy in electricity generation and electric vehicles (EVs) in transportation. The main operational problem associated with a high wind penetration and EVs comes from intermittency and unpredictability. The power systems are likely to face increasing uncertainties in both generation and load sides and there is no coordination between them. In addition, EVs might impose excessive load on the grid. Therefore, coordinating the EV aggregator with the generating companies in the electricity market can enhance the stability of the power system via unidirectional vehicle-to-grid (V2G) technology. This thesis concentrates on the impact of the participation of the EV load aggregator and wind power, and the coordination strategy on the market outcomes and prices. Firstly, power exchange between the wind generating companies (WGenCos) and EV load aggregators considered as price-takers in the energy and ancillary service markets is modelled and analysed. A two-stage stochastic linear programming-based optimal offering/bidding strategy model is developed for the coordinated EV-Wind units participating in the day-ahead energy, balancing, and regulation markets. In future electricity markets, the EV aggregator will have a more important role with high penetration of EV numbers. Finally, the EV aggregator as price-maker which is in generation portfolio of single and multiple strategic firms including WGenCo and conventional generating companies (CGenCos) is modelled and investigated. A stochastic optimal bidding/offering strategy is developed for the EV load aggregator providing the energy and ancillary services in coordination with single and multiple strategic firms in a pool-based electricity market with endogenous formation of day-ahead and real-time prices, and EV aggregator tariff. The methodology consists of using stochastic optimization categorized into single and multiple optimization problems. In the single optimization problem, WGenCo and EV aggregator considered as price-takers aim to maximize their objective function associated with equality or inequality constraints. In multiple optimization problems, the strategic firms such as WGenCos, EV aggregators, and other players considered as price-makers, submit supply-offers/demand-bids to the market operator to participate in the electricity market. A bilevel (hierarchical) model is used in this thesis to model the behaviour of each player. A bilevel problem includes an upper-level problem and a set of lower-level problems which are limited by the upper and lower equality and inequality constraints. Throughout the thesis, both analytical proofs and numerical examples are provided to review the market analysis of EV aggregator, CGenCo and WGenCo and the coordination strategy. The numerical results show the effectiveness of the coordination strategy, which is profitable and beneficial with increasing EV penetration in comparison with the incoordination strategy. We conclude that EV aggregators as an individual firm could not compete with other conventional, dispatchable companies. Hence, merging EV aggregators in CGenCos' and WGenCos' portfolio would increase the payoff of EV aggregators and strategic firms. However, a sufficient EV number is a significant factor to affect market and EV aggregator outputs. Moreover, the numerical results show that the EV tariff and numbers at EV-level can influence the market price and power generation at wholesale-level in the electricity market. In addition, the high penetration of EVs leads to increasing the wind power penetration and reducing the wind power curtailment.


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