Energy management and economic analysis of multiple energy storage systems in solar PV/PEMFC hybrid power systems

Abstract

This study proposes an energy management system (EMS) to manage a standalone hybrid power system (HPS) comprising solar photovoltaic (PV), proton exchange membrane fuel cell (PEMFC), and a battery energy storage. The battery and a hydrogen storage system in PEMFC provide short-and long-term electricity storage, respectively. The EMS ensures electrical efficiency during normal/abnormal operation of the HPS and related limitations, namely unexpected variations in solar irradiance and loads, PEMFC cold start, and battery charging status. The proposed EMS is evaluated using mathematical modelling, simulation studies, and real-time digital simulation. The results show that the proposed EMS is resilient to complex solar PV/load power changes and keeps the dc-link voltage in place. The viability of the proposed HPS is analysed using daily average solar insolation and load profiles. This analysis is useful to schedule problems of load/generation. The economic analysis of the proposed EMS, done using Homer pro, ensures that the HPS operates cost-effectively.

Optimal power bidding of overseas PV plants in Singapore wholesale electricity market

“…For the second question, existing methods are mainly used for optimising the entire power network (e.g. a microgrid) [6,7] or hybrid systems [8] with renewables, including PV and ESS, as well as other resources, which cannot be directly applied to the power bidding problem. A recent study [9] reported a data-driven method for energy bidding in the electricity market; however, this method was designed for a microgrid, not for the case in this study.…”

Mobile battery energy storage system control with knowledge‐assisted deep reinforcement learning

“…Constraint (7) ensures that the device cannot be both moving and parking; Constraint (8) ensures that the arrival and departure places are consistent with the parking place; Constraint (9) ensures that the device cannot be both arriving and departing at the same time and place; Constraint (10) ensures that the moving variable is consistent with the departure variable; Constraints (11) and (12) ensure that the arrival variable is consistent with the moving variable; and Constraint (13) represents the travel-time constraint.…”

“…MBESS scheduling is complicated owing to complex spatiotemporal characteristics and uncertainties of different systems and environments, such as transportation congestion, electricity price volatility [5], renewable energy fluctuations [6], and energy storage systems [7]. Inconsistent movement and charge‐discharge frequencies as well as system uncertainty modelling are the two main difficulties of the MBESS problem [8, 9].…”