A Hybrid Optimization Approach to Demand Response Management for the Smart Grid

TL;DR

This paper introduces a hybrid bilevel optimization method combining genetic algorithms and distributed algorithms for demand response management in smart grids, improving retailer profits and reducing customer bills.

Contribution

It presents a comprehensive energy management system and a hybrid optimization approach to solve the bilevel pricing problem in demand response.

Findings

Enhanced retailer profit through optimized pricing.

Reduced customer energy bills.

Validated effectiveness via numerical simulations.

Abstract

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.