Optimal Operational Planning of Energy Plants Considering Uncertainty of Renewable Energy Outputs by Global-best Brain Storm Optimization. Issue 4 (2019)

• Record Type: Journal Article
• Authors: Arai, Kiyo; Fukuyama, Yoshikazu; Iizaka, Tatsuya; Matsui, Tetsuro
• Is Part Of: IFAC-PapersOnLine. Volume 52:Issue 4(2019)
• Page Start: 153
• Page End: 158
• Publication Date: 2019
• Subjects: Energy management systems -- Optimization problem -- Heuristic searches -- Renewable energy systems -- Uncertainty -- Monte Carlo Simulation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2019.08.171
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper proposes optimal operational planning of energy plants considering (OPEP) renewable energy (RE)'s uncertainty. In recent years, global warming is exacerbated by the increase in the carbon dioxide's emission which belongs to greenhouse gases. Utilization of renewable energies is necessary in order to reduce its emissions. RE can also be utilized as emergency power supplies for Business Continuity Planning (BCP). Since the deterministic operational planning problem of energy plants is one of mixed-integer nonlinear optimization programming (MINLP) problems, conventional mathematical programming cannot solve it easily. Hence, it has been solved by various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), modified brain storm optimization (MBSO), and global-best brain storm optimization (GBSO). In addition, considering uncertainty of RE outputs, Monte Carlo simulation should be utilized. The proposed Monte Carlo simulation based method using GBSO is compared with the deterministic GBSO based method. It was verified the proposed Monte Carlo simulation based method using GBSO can consider uncertainty of renewable energies appropriately.