  In future energy systems characterized by significant shares of fluctuating
renewable energy sources, there is a need for a fundamental change in
electricity consumption. The energy system requires the ability to adapt to the
intermittent electricity generation of renewable energy sources. This can be
achieved by integrating flexible electrical loads, such as electric heating
devices and electric vehicles, in combination with efficient control methods.
In this paper, we introduce the Pareto local search method PALSS with heuristic
search operations to solve the multi-objective optimization problem of a
residential area with different types of flexible loads. PALSS shifts the
flexible electricity load with the objective of minimizing the electricity cost
and peak load while maintaining the inhabitants' comfort in favorable ranges.
Further, we include reinforcement learning into the heuristic search operations
in the approach RELAPALSS and use the dichotomous method for obtaining all
Pareto-optimal solutions of the multi-objective optimization problem with
conflicting goals. The methods are evaluated in simulations with different
configurations of the residential area. The results show that PALSS and
RELAPALSS strongly outperform the two multi-objective evolutionary algorithms
NSGA-II and SPEA-II from the literature and the conventional control approach.
The inclusion of reinforcement learning in RELAPALSS leads to additional
improvements. Our study reveals the need for multi-objective optimization
methods to utilize renewable energy sources in residential areas.
