Hamidreza Jahangir

Gabriel D. Patrón, Di Zhang, Lavinia M. P. Ghilardi et al.

Energy storage promotes the integration of renewables by operating with charge and discharge policies that balance an intermittent power supply. A key challenge in this emerging sector is how to optimize the operation of storage assets given future price uncertainties and the need to recover the costs of project finance while ensuring an attractive return on equity and hedging against downside risk. This study investigates the scheduling of energy storage assets under price uncertainty, with a focus on electricity markets. A two-stage stochastic risk-constrained approach is employed, whereby electricity price trajectories or specific power markets are observed, allowing for recourse in the schedule. Conditional value-at-risk is used to quantify risk in the optimization problems; this allows for explicit specification of a probabilistic risk limit. The proposed approach is tested in an integrated hydrogen system (IHS) and a battery energy storage system (BESS). In the joint design and operation context for the IHS, the risk constraint results in large installed unit capacities, increasing capital cost but enabling more inventory to buffer price uncertainty. In both case studies, there is an operational trade-off between risk and expected reward; this is reflected in higher expected costs (or lower expected profits) with increasing risk aversion. Despite the decrease in expected reward (up to 500\$k), both systems exhibit substantial benefits of increasing risk aversion (up to 1.5\$mn) with respect to risk-neutral settings. This work provides a general method to address uncertainties in energy storage scheduling, allowing operators to input their level of risk tolerance on asset decisions.

Subhash Lakshminarayana, Saurav Sthapit, Hamidreza Jahangir et al.

Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large-scale IoT-based load-altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data-driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics-informed neural networks (PINN), employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6-,14- and 39-bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines (SVM), and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.