Optimum planning and operation of compressed air energy storage with wind energy integration

The integration of increasingly available renewable energy sources, such as wind energy, into the power grid will have the potential to reduce dependence on fossil fuels and minimize greenhouse gas emission. However, due to the stochastic nature of renewable generation, balancing of generation and load becomes difficult. Energy storage is expected to play a major role in promoting the development of renewable energy by intermittent power source balancing, storing surplus generation, and providing electricity during high demands. One of the various emerging energy storage technologies is Compressed Air Energy Storage (CAES). In this paper, we model a wind generation-CAES system which can generate, store, and sell electricity to the grid. In addition, two optimization methodologies based on particle swarm optimization (PSO) are used to optimize the short-term operation and long-term planning of the wind generation-CAES system. The goal is to determine the optimum capacities of these resources as well as the optimum day-to-day operation strategy in order to maximize profit. The variables considered in this study include electricity market price, wind speed, gas price, etc., from a local electric utility. A number of sensitivity analyses are performed to evaluate the profitability of the wind generation-CAES system and the impact of different factors on the results.