Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty

• Published in: Energies (Basel) Vol. 13; no. 22; p. 6052
• Main Authors: Ryu, Ho-Sung, Kim, Mun-Kyeom
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2020
• Subjects: Algorithms; Alternative energy; conditional value at risk; Consumption; Demand side management; Distributed generation; Energy industry; Energy management; Energy resources; Energy sources; improved multi-layer artificial bee colony algorithm; Linear programming; Load; Monte Carlo simulation; Multilayers; Numerical analysis; Operating costs; Operators; Optimization; Renewable resources; Reserve requirements; Risk; Risk aversion; risk-based hybrid demand response; Solar energy; Standard deviation; two-stage optimization; uncertainties; Uncertainty
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13226052

Owing to the increasing utilization of renewable energy resources, distributed energy resources (DERs) become inevitably uncertain, and microgrid operators have difficulty in operating the power systems because of this uncertainty. In this study, we propose a two-stage optimization approach with a hybrid demand response program (DRP) considering a risk index for microgrids (MGs) under uncertainty. The risk-based hybrid DRP is presented to reduce both operational costs and uncertainty effect using demand response elasticity. The problem is formulated as a two-stage optimization that considers not only the expected operation costs but also risk expense of uncertainty. To address the optimization problem, an improved multi-layer artificial bee colony (IML-ABC) is incorporated into the MG operation. The effectiveness of the proposed approach is demonstrated through a numerical analysis based on a typical low-voltage grid-connected MG. As a result, the proposed approach can reduce the operation costs which are taken into account uncertainty in MG. Therefore, the two-stage optimal operation considering uncertainty has been sufficiently helpful for microgrid operators (MGOs) to make risk-based decisions.