  In this paper, we study the strategic investment problem of battery energy
storage systems (BESSs) in the wholesale electricity market from the
perspective of BESSs owners. Large-scale BESSs planning without considering the
possible wholesale market price change may result in possible locational
marginal price (LMP) changes. To overcome such limits, we propose a three-phase
approach for the BESS investment problem. In Phase-1, we conduct a search for
the optimal BESS configurations via a congestion-based heuristics and Bayesian
optimization. In Phases 2 and 3, we alternatively dispatch optimization tasks
to optimize the wholesale market clearing for LMPs and identify the optimal
schedule for BESSs operations. We validate the model using a ten-year
simulation on the Electric Reliability Council of Texas (ERCOT) market.
Experimental results show that the Bayesian optimization model runs 16 times
faster than the grid search model in achieving the same solution quality.
Further, the iterative method demonstrates that the model without considering
possible LMP changes makes a 21% profit overestimation.
