Heuristic Optimization for an Aggregator-Based Resource Allocation in the Smart Grid

We utilize a for-profit aggregator-based residential demand response (DR) approach to the smart grid resource allocation problem. The aggregator entity, using a given set of schedulable residential customer assets (e.g., smart appliances), must set a schedule to optimize for a given objective. Here, we consider optimizing for the profit of the aggregator. To encourage customer participation in the residential DR program, a new pricing structure named customer incentive pricing (CIP) is proposed. The aggregator profit is optimized using a proposed heuristic framework, implemented in the form of a genetic algorithm, that must determine a schedule of customer assets and the CIP. To validate our heuristic framework, we simulate the optimization of a large-scale system consisting of 5555 residential customer households and 56,642 schedulable assets using real-pricing data over a period of 24-h. We show that by optimizing purely for economic reasons, the aggregator can enact a beneficial change on the load profile of the overall power system.