Probabilistic adaptive model predictive power pinch analysis (PoPA) energy management approach to uncertainty. Issue 17 (4th April 2019)

• Record Type: Journal Article
• Authors: Bassey Etim, Nyong‐Bassey; Giaouris, Damian; Patsios, Haris; Gadoue, Shady; Papadopoulos, Athanasios I.; Seferlis, Panos; Voutetakis, Spyros; Papadopoulou, Simira
• Is Part Of: Journal of engineering. Volume 2019:Issue 17(2019)
• Page Start: 4288
• Page End: 4292
• Publication Date: 2019-04-04
• Subjects: stochastic processes -- predictive control -- hybrid power systems -- Monte Carlo methods -- energy management systems -- renewable energy sources -- adaptive control; predictive control horizon -- robust energy management strategy -- bounded predicted power grand composite curve -- energy management strategies -- adaptive power pinch strategy -- probabilistic adaptive model predictive power pinch analysis energy management approach -- PoPA -- probabilistic power pinch analysis approach -- Monte–Carlo simulation -- hybrid energy systems uncertainty -- systems power grand composite curve -- DA -- MCS; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8154
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day‐ahead (DA) and adaptive power pinch strategy.