Electricity auctions with intermittent wind generation

More and more wind generation is introduced to electricity markets. Yet, it is not clear how to conduct auctions with significant stochastic wind generation while avoiding dramatically increasing computational complexity. In this paper, the Markov process concept is innovatively used to model wind generation with transition matrices established based on the historical data. Wind generation is then integrated into system demand, and the day-ahead auction problem considering system demand, unit maximal and minimal generations and ramp rates is formulated. The goal is to optimize the commitment and dispatch of normal generators to minimize the total expected cost while satisfying all possible loads. Lagrangian relaxation is used to demonstrate the capability of the formulation. Since there are many states at each hour, a novel enhanced version of Dynamic Programming is developed to solve the subproblems. Numerical testing results obtained by CPLEX demonstrate the effectiveness and scalability of the method compared with the standard stochastic programming formulation based on scenarios, and the feasibility compared with the deterministic formulation.