Optimal dispatch of virtual power plant using interval and deterministic combined optimization. (November 2018)

• Record Type: Journal Article
• Authors: Liu, Yangyang; Li, Min; Lian, Hongbo; Tang, Xiaowei; Liu, Chuanquan; Jiang, Chuanwen
• Is Part Of: International journal of electrical power & energy systems. Volume 102(2018)
• Page Start: 235
• Page End: 244
• Publication Date: 2018-11
• Subjects: Optimal dispatch model -- Virtual power plant -- Interval optimization -- Deterministic optimization; 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.2018.04.011
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: An interval and deterministic combined optimization was proposed. The dispatch model maximizes the VPPs' profit intervals and deterministic profits. This combined optimization was compared with other optimization approaches. Abstract: Virtual power plant (VPP) technology is a promising solution to manage the uncertainties of renewable energy in demand side. Because of various uncertainties, VPPs' dispatch models are always solved by stochastic optimization, robust optimization and interval optimization. However, these approaches always require high computational complexity, be over conservative or cannot describe VPPs' profitability precisely. Thus, this paper combined interval and deterministic optimization together and adopted the combined approach to solve a VPP's dispatch problem. The combined optimization not only maximized VPPs' deterministic profits under forecasted scenarios to estimate the VPP's most likely profits, but also maximized VPPs' profit intervals to manage uncertainties. The proposed model was in a regulated electricity market environment, and the VPP's traded energy was cleared by time-of-use prices. A case study from real world was adopted to prove the validity of this model. Comparison with other optimizations like stochastic and robust optimization was also studied. The combined optimization can manage the VPP's uncertainties within limited computational time.