Risk-based hybrid energy management with developing bidding strategy and advanced demand response of grid-connected microgrid based on stochastic/information gap decision theory. (October 2021)

• Record Type: Journal Article
• Authors: Kim, H.J.; Kim, M.K.
• Is Part Of: International journal of electrical power & energy systems. Volume 131(2021)
• Publication Date: 2021-10
• Subjects: Risk-based hybrid energy management -- Bidding strategy -- Uncertainties -- Stochastic/information gap decision theory -- Confidence-based demand response; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2021.107046
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Risk-based hybrid energy management of grid-connected MG is proposed. Optimal staircase bidding profile of grid-connected MG is provided based on S/IGDT. Robustness/opportunity functions can be selected depending on risk tolerance. The effects of proposed CIDR program on the proposed framework are analyzed. 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.