Computational Intelligence for Demand Response Exchange Considering Temporal Characteristics of Load Profile via Adaptive Fuzzy Inference System
作者： Srikanth Reddy K. , Lokesh Kumar Panwar , B.K. Panigrahi , Rajesh Kumar
DOI: 10.1109/TETCI.2017.2739128
关键词: Fuzzy logic 、 Computational intelligence 、 Availability factor 、 Heuristic (computer science) 、 Adaptive neuro fuzzy inference system 、 Activity-based costing 、 Computer science 、 Load management 、 Load profile 、 Data mining
摘要: This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed considers profile utilization factor, availability factor alongside conventional/traditional willingness factor. relation between criticality and flexibility terms factors has been established incorporated into DR seller/customer bids DRX through dynamic costing. Various models viz. linear, nonlinear, exponential model etc., are developed assess varying behavior with respect CBL profile. In addition, FIS is this account for uncertain/indistinct nature input/information provided by customer. To improve performance market clearing, parameters membership functions used adaptively tuned heuristic approaches. compared traditional approach, fuzzy, nonfuzzy without considering characteristics, approaches only. simulation results presented they demonstrate superiority based costing when other models.
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