Biblio

The accelerated penetration rate of renewable energy sources (RES) brings environmental benefits at the expense of increasing operation cost and undermining the satisfaction of the N-1 security criterion. To address the latter issue, this paper extends the state of the art, i.e. deterministic AC security-constrained optimal power flow (SCOPF), to capture two new dimensions: RES stochasticity and inter-temporal constraints of emerging sources of flexibility such as flexible loads (FL) and energy storage systems (ESS). Accordingly, the paper proposes and solves for the first time a new problem formulation in the form of stochastic multi-period AC SCOPF (S-MP-SCOPF). The S-MP-SCOPF is formulated as a non-linear programming (NLP). It computes optimal setpoints in day-ahead operation of flexibility resources and other conventional control means for congestion management and voltage control. Another salient feature of this paper is the comprehensive and accurate modelling: AC power flow model for both pre-contingency and post-contingency states, joint active/reactive power flows, inter-temporal resources such as FL and ESS in a 24-hours time horizon, and RES uncertainties. The applicability of the proposed model is tested on 5-bus (6 contingencies) and 60 bus Nordic32 (33 contingencies) systems.

The emergence of the 5G network has boosted the advancements in the field of the internet of things (IoT) and edge/cloud computing. We present a novel architecture to detect fire in indoor and outdoor environments, dubbed as EAC-FD, an abbreviation of edge and cloud-based fire detection. Compared with existing frameworks, ours is lightweight, secure, cost-effective, and reliable. It utilizes a hybrid edge and cloud computing framework with Intel neural compute stick 2 (NCS2) accelerator is for inference in real-time with Raspberry Pi 3B as an edge device. Our fire detection model runs on the edge device while also capable of cloud computing for more robust analysis making it a secure system. We compare different versions of SSD-MobileNet architectures with ours suitable for low-end devices. The fire detection model shows a good balance between computational cost frames per second (FPS) and accuracy.