  In this paper we present a novel methodology for leveraging Receding Horizon
Control (RHC), also known as Model Predictive Control (MPC) strategies for
distributed battery storage in a planning problem using a Benders decomposition
technique. Longer prediction horizons lead to better storage placement
strategies but also higher computational complexity that can quickly become
computationally prohibitive. The here proposed MPC strategy in conjunction with
a Benders decomposition technique effectively reduces the computational
complexity to a manageable level. We use the CIGRE low voltage (LV) benchmark
grid as a case study for solving an optimal placement and sizing problem for
different control strategies with different MPC prediction horizons. The
objective of the MPC strategy is to maximize the photovoltaic (PV) utilization
and minimize battery degradation in a local residential area, while satisfying
all grid constraints. For this case study we show that the economic value of
battery storage is higher when using MPC based storage control strategies than
when using heuristic storage control strategies, because MPC strategies
explicitly exploit the value of forecast information. The economic merit of
this approach can be further increased by explicitly incorporating a battery
degradation model in the MPC strategy.
