VI. CONCLUSION
The balancing of energy utilization is one of the most important
factors for efficient operation of a smart grid. Demand response
uses real time scheduling to enable customers to modify their
demand according to energy consumption costs. This paper
proposed a multi-objective optimization approach for efficient
energy scheduling in smart grids. We formulated the energy
scheduling problem as a constrained multi-objective
optimization problem. The first objective is minimization of
energy consumption and the second objective is maximization
of utility. We adapted an evolutionary multi-objective
algorithm called EMOCA for computing Pareto-optimal energy
schedules subject to a constraint on generation capacity. We
performed extensive simulations in Matlab to evaluate the
performance of EMOCA. Our simulations clearly demonstrate
the efficiency of multi-objective evolutionary algorithms in
discovering multiple non-dominated solutions characterizing
the tradeoffs between energy consumption and utility. The
results also show that EMOCA outperforms a well known multi
objective evolutionary algorithm NSPEA-II