# Towards multiobjective optimization and control of smart grids

## TL;DR

This paper explores multiobjective optimization for smart grids, balancing load shaping and flexibility using Pareto optimality to analyze trade-offs and improve grid robustness and resilience.

## Contribution

It introduces a multiobjective framework employing Pareto optimality to analyze and manage trade-offs in smart grid energy storage control.

## Key findings

- Pareto frontier analysis quantifies trade-offs between objectives.
- Multiobjective approach improves balancing of load shaping and flexibility.
- Framework enhances robustness and resilience of smart grids.

## Abstract

The rapid uptake of renewable energy sources in the electricity grid leads to a demand in load shaping and flexibility. Energy storage devices such as batteries are a key element to provide solutions to these tasks. However, typically a trade-off between the performance related goal of load shaping and the objective of having flexibility in store for auxiliary services, which is for example linked to robustness and resilience of the grid, can be observed. We propose to make use of the concept of Pareto optimality in order to resolve this issue in a multiobjective framework. In particular, we analyse the Pareto frontier and quantify the trade-off between the non-aligned objectives to properly balance them.