Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles
<|reference_start|>Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles: 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.<|reference_end|>
@article{dengiz2024pareto,
    title={Pareto local search for a multi-objective demand response problem in
  residential areas with heat pumps and electric vehicles},
    author={Thomas Dengiz, Andrea Raith, Max Kleinebrahm, Jonathan Vogl, Wolf
  Fichtner},
    journal={arXiv preprint arXiv:2407.11719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11719},
    primaryClass={eess.SY cs.SY}
}