Aggregated Demand Response Scheduling in Competitive Market Considering Load behavior through Fuzzy Intelligence

Abstract

This article presents an integrated and intelligent price based self-scheduling approach for demand response (DR) aggregators in the wholesale market considering load profile attributes of aggregated loads. The load profile attributes namely utilization and availability factors are derived as a function of load type and temporal characteristics of responsive loads. The proposed integrated approach uses the concept of conventional random willingness factors coupled with load profile attributes to resolve the ambiguity around customer/load behavior. The response of the customer to load profile attributes and the willingness factor is formulated using three different models, namely, linear, exponential, and nonlinear models. Further, to consider the nondeterministic and indistinct behavior of the customer, a fuzzy inference system (FIS) is developed to create a relationship between the load profile attributes and willingness to DR participation cost. Further, an online FIS membership function parameter tuning mechanism is developed to improve the performance of DR aggregator as well as overall day ahead market. The proposed approach is simulated for a combined price based scheduling of the generation company and the DR aggregator with responsive loads spread over various load sectors such as industrial, commercial, residential, agricultural, and the municipal sector. The simulation results of the online intelligent-integrated framework are compared to conventional willingness model, nonfuzzy, and untuned FIS models with and without considering load profile and customer willingness factor. The comparison of the same demonstrates the effectiveness of the proposed online framework in improving the overall and DR aggregator surplus over other approaches.