Energy consumption

Quantum mechanics

Context (archaeology)

Engineering

Biology

Statistics

Environmental economics

Predictability

Peak demand

Demand reduction

Smart grid

Economics

Computer science

Paleontology

Demand response

Physics

Mathematics

Management

Sociology

Automotive engineering

Environmental science

Operations management

Geometry

Medicine

Metric (unit)

Consumption (sociology)

Load shifting

Flexibility (engineering)

Electricity

Reduction (mathematics)

Electrical engineering

Pathology

Social science

Applied Energy

Demand response (DR) is considered an effective approach in mitigating the ever-growing concerns for supplying the electricity peak demand. Recent attempts have shown that the contribution from the aggregate impact of flexible individual residential loads can add flexibility to the power grid as ancillary services. However, current DR schemes do not systematically distinguish the varying potential of user contribution due to the highly-varied usage behaviors. Thus, this paper proposes a data-driven approach for quantifying the potential of individual flexible load users for participation in DR. We introduced a metric to capture the predictability of usage in a future DR event using the historical consumption data for different load types. The metric helps to sort the users with flexible loads in a community according to their potential for load shifting scenarios. We then evaluated the applicability of the metric in the DR context to assess the extent of energy reduction for different segments of users. In our analysis, we included electric vehicle, wet appliances (dryer, washing machine, dishwasher), and air conditioning. The analysis of real-world data shows that the approach is effective in identifying suitable user segments with higher predictive potential for demand reduction. We also presented a cross-appliance comparison for assessing the flexibility potential of different user segments. As a case study based on Pecan Street Project, the findings suggest that potentially ~160 MWh reduction might be achieved in Austin, TX through only 20% participation of the selected flexible loads for the residential sector during a 2-h event.

Residential loads flexibility potential for demand response using energy consumption patterns and user segments