  This paper proposes a hybrid approach to optimal day-ahead pricing for demand
response management. At the customer-side, compared with the existing work, a
detailed, comprehensive and complete energy management system, which includes
all possible types of appliances, all possible applications, and an effective
waiting time cost model is proposed to manage the energy usages in households
(lower level problem). At the retailer-side, the best retail prices are
determined to maximize the retailer's profit (upper level problem). The
interactions between the electricity retailer and its customers can be cast as
a bilevel optimization problem. To overcome the weakness and infeasibility of
conventional Karush--Kuhn--Tucker (KKT) approach for this particular type of
bilevel problem, a hybrid pricing optimization approach, which adopts the
multi-population genetic algorithms for the upper level problem and distributed
individual optimization algorithms for the lower level problem, is proposed.
Numerical results show the applicability and effectiveness of the proposed
approach and its benefit to the retailer and its customers by improving the
retailer's profit and reducing the customers' bills.
