Impact of Load Profile on Dynamic Interactions Between Energy Markets: A Case Study of Power Exchange and Demand Response Exchange

• Published in: IEEE transactions on industrial informatics 2019-11, Vol.15 (11), p.5855-5866
• Main Authors: Konda, Srikanth Reddy, Al-Sumaiti, Ameena Saad, Panwar, Lokesh Kumar, Panigrahi, Bijaya Ketan, Kumar, Rajesh
• Format: Article
• Language: English
• Subjects: Adaptive systems; Clearing; Computer simulation; Convergence; Customer baseline load; Decision making; demand response; demand response exchange; Elasticity; Energy management; energy markets; Exchanging; Fuzzy systems; independent system operator; Indexes; ISO; Iterative methods; Job shop scheduling; Load; Load management; Load modeling; load profiling; Operating costs; Performance enhancement; Power markets; Scheduling
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2019.2910349

Demand response exchange (DRX) presents a pool-based approach for optimal clearing/scheduling of demand response (DR) services through bid clearing. The same is intended to minimize the limitations of price-based and incentive-based mechanisms. However, unlike the price-based or incentive-based DR schemes, the DR sellers in DRX do not have a clear motivation to formulate the bid offers. Similarly, the DR buyers' bid formulation should also include optimal benefit analysis. This paper formulates and analyzes the bid formulation for DR sellers and buyers considering respective operation objectives. The DR seller bid formulation is derived considering load behavior through utilization index and availability index obtained from load profile of respective loads. On the other hand, DR buyer bid formulation is derived from power exchange operation attributes/cost of generation. Therefore, the DR clearing ultimately affects the operational cost of operation which, in turn, is reflected in DR buying bid and DR clearing finally. This paper develops an iterative approach in which the continuous interactions between ISO/power exchange would finally converge to a stable operation. The load profile-based strategic bid formulation is modeled using mathematical as well as fuzzy inference system (FIS). In addition, an adaptive FIS has been devised to improve the performance of DR scheduling. The simulation results illustrate the impact of customer behavior, intelligent decision-making, and penetration level on the performance and convergence of power markets (DRX and power exchange).