# Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

## TL;DR

This paper introduces a multiobjective optimization approach for demand response in smart grids, balancing residential energy costs and load factors to improve grid efficiency and user satisfaction.

## Contribution

It develops a novel multiobjective evolutionary algorithm that effectively generates Pareto optimal demand response solutions considering energy costs and load factors.

## Key findings

- Outperforms comparable methods in reducing energy costs.
- Achieves a balanced load factor for the grid.
- Produces Pareto optimal demand response programs.

## Abstract

Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residential users and the load factor of the underlying grid. A multiobjective optimization problem (MOP) is formulated and Pareto optimality is adopted. Stochastic search methods of generating feasible values for decision variables are proposed. Theoretical analysis is performed to show that the proposed methods can effectively generate and preserve feasible points during the solution process, which comparable methods can hardly achieve. A multiobjective evolutionary algorithm is developed to solve the MOP, producing a Pareto optimal demand response (PODR) program. Simulations reveal that the proposed method outperforms the comparable methods in terms of energy costs while producing a satisfying load factor. The proposed PODR program is able to systematically balance the needs of the grid and residential users.