This paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high-penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bilevel ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bilevel model is formulated as a mathematical program with equilibrium constraints) and then recast into a mixed-integer linear programming using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin-based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bilevel model. The results from the conventional model and the bilevel model are compared under different ES power and energy ratings, and also various load and wind penetration levels.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Energy storage
- locational marginal price (LMP)
- mathematic program with equilibrium constraints (MPEC)
- price arbitrage potential