  Energy storage can facilitate the integration of renewable energy resources
by providing arbitrage and ancillary services. Jointly optimizing energy and
ancillary services in a centralized electricity market reduces the system's
operating cost and enhances the profitability of energy storage systems.
However, achieving these objectives requires that storage be located and sized
properly. We use a bi-level formulation to optimize the location and size of
energy storage systems which perform energy arbitrage and provide regulation
services. Our model also ensures the profitability of investments in energy
storage by enforcing a rate of return constraint. Computational tractability is
achieved through the implementation of a primal decomposition and a
subgradient-based cutting-plane method. We test the proposed approach on a
240-bus model of the Western Electricity Coordinating Council (WECC) system and
analyze the effects of different storage technologies, rate of return
requirements, and regulation market policies on ES participation on the optimal
storage investment decisions. We also demonstrate that the proposed approach
outperforms exact methods in terms of solution quality and computational
performance.
