Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory
- Authors
- Kim, H.J.; Kim, M.K.
- Issue Date
- Oct-2021
- Publisher
- Elsevier Ltd
- Keywords
- Bidding strategy; Confidence-based demand response; Risk-based hybrid energy management; Stochastic/information gap decision theory; Uncertainties
- Citation
- International Journal of Electrical Power and Energy Systems, v.131
- Journal Title
- International Journal of Electrical Power and Energy Systems
- Volume
- 131
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53998
- DOI
- 10.1016/j.ijepes.2021.107046
- ISSN
- 0142-0615
1879-3517
- Abstract
- This study evaluates a risk-based hybrid energy management problem by creating a staircase bidding profile for microgrid operators under a confidence-based incentive demand response program. Scenario-based modeling of photovoltaic, wind turbine, and local loads is achieved by implementing a stochastic/information gap decision theory-based optimization technique; the upstream grid price uncertainty is accounted for, based on the errors between the actual and predicted values. By employing a demand response aggregator, the proposed demand response can be applied to reduce the total expected operating cost and enhance the reliability of the microgrid peak-period load, primarily through peak-period load reduction. To demonstrate the applicability and validate the effectiveness of the proposed risk-based hybrid energy management problem, a case study is analyzed and solved by applying an improved particle swarm optimization algorithm. The results demonstrate that the proposed framework can pursue risk-neutral, risk-averse, and risk-seeker strategies to provide microgrid operator with more degrees of freedom for hedging against risks. In addition, to manage price uncertainty in the optimal scheduling of grid-connected microgrid, operators can build staircase bidding curves that can be effectively submitted to the day-ahead market. Further comparative analysis reveals that the proposed method demonstrates superior solution quality and diversity with a reduced computational burden. © 2021 Elsevier Ltd
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