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Recently, photovoltaic (PV) with energy storage systems (ESS) have been widely adopted in buildings to overcome growing power demands and earn financial benefits. The overall energy cost can be optimized by combining a well-sized hybrid PV/ESS system with an efficient energy management system (EMS). Generally, EMS is implemented within the overall functions of the Building Automation System (BAS). However, due to its limited computing resources, BAS cannot handle complex algorithms that aim to optimize energy use in real-time under different operating conditions. Furthermore, islanding the building's local network to maximize the PV energy share represents a challenging task due to the potential technical risks. In this context, this article addresses an improved approach based on upgrading the BAS data analytics capability by means of an edge computing technology. The edge communicates with the BAS low-level controller using a serial communication protocol. Taking advantage of the high computing ability of the edge device, an optimization-based EMS of the PV/ESS hybrid system is implemented. Different testing scenarios have been carried out on a real prototype with different weather conditions, and the results show the implementation feasibility and technical performance of such advanced EMS for the management of building energy resources. It has also been proven to be feasible and advantageous to operate the local energy network in island mode while ensuring system safety. Additionally, an estimated energy saving improvement of 6.23 % has been achieved using optimization-based EMS compared to the classical rule-based EMS, with better ESS constraints fulfillment.
Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes.
Proton Exchange Membrane Fuel Cell (PEMFC) is one of the most promising technologies for sustainable energy production due to the high power density, low operative temperature and more convenient use for several applications. Nevertheless, the high generated current that characterizes PEMFC requires a specific power conditioning. In addition, specific controller must be designed to fit with system operative points changing associated with the variation of this high current. To deal with this challenge, in this paper, an electrochemical system composed of a Proton Exchange Membrane Fuel Cell (PEMFC) feeding via two phases IBC has been proposed and investigated. For robustness, the used IBC for fuel cell voltage regulation is controlled by linear quadratic regulator (LQR). Then, genetic algorithms technique is applied to optimize the LQR controller parameters giving optimal control coefficients and can if necessary be adjusted according to each working situation change. The model of the entire system is studied using Matlab/Simulink environment. The simulation’s comparative standard and robustness results both demonstrate that the proposed GA-based LQR controller outperforms the conventional PI in terms of performance metrics (overshoot reduction: between 58.93% and 97.09%; response time reduction: between 56.40% and 77.00% and ripple reduction: between 84.00% and 94.86%).