Optimal Placement and Sizing of Distributed Battery Storage in Low Voltage Grids using Receding Horizon Control Strategies
Philipp Fortenbacher, Andreas Ulbig, G\"oran Andersson

TL;DR
This paper introduces a novel MPC-based methodology combined with Benders decomposition for optimal placement and sizing of distributed battery storage in low voltage grids, improving economic value and computational efficiency.
Contribution
It presents a new MPC strategy with Benders decomposition for efficient, optimal battery placement and sizing in low voltage grids, considering forecast information and degradation effects.
Findings
MPC strategies outperform heuristic controls in economic value.
Longer prediction horizons improve storage placement strategies.
The approach reduces computational complexity for large-scale problems.
Abstract
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,…
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