Collaborative demand response management is an effective method to lower the peak-to-average ratio of demand and to facilitate the integration of locally distributed renewable energy resources to the electricity grid. The aggregator needs a holistic and privacy-preserving demand response management scheme to involve residential customers in a dynamic pricing market scenario. Using a quadratic function to model dynamic pricing, we propose a two-level distributed energy management scheme for a residential community to exploit the benefits of coordination among customers at the aggregator level and the smart devices at the customer level. In the proposed scheme, each customer wants to optimize the scheduling of its smart appliances, demand flexibility of air conditioning load, and energy storage strategies to minimize their expected cost, discomfort and appliance interruption. The aggregator, on the other hand, seeks to minimize the overall expected cost by optimizing customers energy demand and its energy storage strategies. The aggregator level optimization is formulated as a noncooperative Stackelberg equilibrium problem with shared constraints. Meanwhile, the customer level problem is formulated as a multiobjective optimization using different discomfort and interruption indicators to characterize various appliance preferences. We formulate iterative algorithms to obtain the appliance scheduling and storage strategies of the customers using genetic algorithm and to reach convergence. Simulation results indicate that the proposed scheme converges while enforcing the shared constraints and reduces the electricity cost to the customers with a quantifiable tradeoff between multiple objectives.

Effective design of air-conditioner (AC) management system has the potential to reduce the cost of electricity consumption and help users to participate in demand response (DR) program as interruptible loads. However, optimizing the operation of AC is complex and, as a potential solution, computational intelligence (CI) techniques based model predictive algorithms are being explored in the literature. This article aims to provide an overview of the CI techniques that are established in addressing relevant and timely open problems of AC management for residential buildings. To do so, first, we provide a brief background on different DR mechanisms and AC management systems. Second, a review of recent advances in CI-based model prediction and optimal control techniques of AC systems for DR management is presented. The discussion reveals that the interest in CI techniques with adaptive learning algorithms is increasing due to their ability to adjust in varying conditions. Then, we provide a brief description of a testbed, which is used for testing various newly developed CI-based AC management techniques in a residential setting. Finally, key issues related to the coordination of a large number of AC systems, modeling accuracy, and computational tractability are highlighted along with their challenges and future research directions.

This article develops a distributional locational marginal pricing (DLMP) based electric vehicle (EV) aggregator scheduling framework minimizing the congestion in the network. The article has two parts: 1) calculation of DLMPs by solving actual DistFlow equations; and 2) EV aggregator scheduling in a distribution network formulated as a bilevel problem considering EV aggregator as a leader and distribution system operator (DSO) as a follower. The upper level problem is the cost minimization of EV aggregator, whereas the lower level problem is the social welfare maximization of DSO while satisfying the network constraints. The nonlinear bilevel problem is reformulated into the single-level problem using Karush–Kuhn–Tucker conditions and duality theorem. The framework considers the uncertainty of the EV aggregator by using robust programming and tested with the 15-bus radial distribution network for congested and uncongested cases.