Risk-Based Robust Bidding Strategies for EVs' Aggregators in Day-ahead
Markets with Uncertainty
Abstract
In the recent electricity market framework, the profit of the generation
companies depends on the decision of the operator on the schedule of its
units, the energy price, and the optimal bidding strategies. Due to the
expanded integration of uncertain renewable generators which is highly
intermittent such as wind plants, the coordination with other facilities
to mitigate the risks of imbalances is mandatory. Accordingly,
coordination of wind generators with the evolutionary Electric Vehicles
(EVs) is expected to boost the performance of the grid. In this paper,
we propose a robust optimization approach for the coordination between
the wind-thermal generators and the EVs in a virtual
power plant (VPP) environment. The objective of maximizing the profit of
the VPP Operator (VPPO) is studied. The optimal bidding strategy of the
VPPO in the day-ahead market under uncertainties of wind power, energy
prices, imbalance prices, and demand is obtained for the worst case
scenario. A case study is conducted to assess the e?effectiveness of the
proposed model in terms of the VPPO’s profit. A comparison between the
proposed model and the scenario-based optimization was introduced. Our
results confirmed that, although the conservative behavior of the
worst-case robust optimization model, it helps the decision maker from
the fluctuations of the uncertain parameters involved in the production
and bidding processes. In addition, robust optimization is a more
tractable problem and does not suffer from
the high computation burden associated with scenario-based stochastic
programming. This makes it more practical for real-life scenarios.