Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

This paper discusses a simulation-based generator-level stability index and demonstrates the use of the index to quantify the impact of different load scenarios, transmission line reactances, and generator parameters on transient stability. A Basin Stability concept is used that is based on recent works in the physics community on complex systems and power systems, which estimates the regions of attraction through time-domain simulation. The quantitative results generated by Basin Stability can provide a general ranking metric to reflect which generator or a group of generators is likely to become unstable after disturbances. A detailed sub-transient model with sixth-order sub-transient generators, second-order exciters, and third-order power system stabilizers has been used for discussion. Additionally, a multi-node basin stability study on a 16-machine 68-bus system with the swing-equation model is also included to demonstrate the replicability. The state-of-the-art parallel computing techniques can significantly reduce the simulation time of the proposed method.

This article presents an approach to address the drawback of stagnation in the local optimum and poor diversity of the Pareto front exhibited in existing heuristic algorithms on optimal power flow problem. We propose an improved grey wolf equilibrium optimizer (GWEO) that integrates the search mechanisms of grey wolf optimization (GWO) and equilibrium optimizer (EO). A disturbance component of EO involves in the search mechanism of GWO to relieve the diversity problem. To validate the algorithm, we implement GWEO to solve the multiobjective optimal power flow (MOOPF) problem in a modified 118-bus distribution system with high distributed photovoltaic (PV) penetration. The objective functions of MOOPF are active power losses, voltage deviation, and PV curtailment rates. Besides, we compare GWEO with GWO, salp swarm algorithm, EO, and hybrid GWO in terms of convergence rate, diversity, and optimum value. The numerical results reveal that our proposed GWEO shows faster convergence rate and better performance on diversity and optimum value, providing decision-makers with better solutions in multiobjective optimization scenarios.

With the increasing number of controllable devices in the grid, devices owned by different entities are supposed to cooperatively support the grid operation. To balance the various interests of the customers and the utility, this paper proposes a two-level scheduling algorithm to address voltage regulation by coordinating the customer-owned battery energy storage system (BESS) and utility-owned load tap changers (LTCs) in power distribution systems. The objective is to find the optimal operation scheduling for both BESSs and LTCs that simultaneously maximize the economic payback and satisfy system operation rules. For BESSs, a local optimization problem is formulated in a chance-constrained manner in major consideration of the economic performance. For the utility interest, a Markov decision process (MDP) and a computationally efficient reinforcement learning (RL) algorithm are utilized to solve the optimal tap setting problem. To coordinate BESSs and LTCs, a global voltage adjustment is applied. The effectiveness of the proposed algorithm is validated via simulations on the IEEE 13-node and 34-node distribution test feeders.

With the expansion of smart grids, and the increasing need for efficient infrastructure management, unmanned aerial vehicles (UAVs) have become vital for tasks such as object detection and smart grid inspections. However, current UAV-based systems face challenges related to environmental factors (e.g., fog, wind, and low visibility) and limited computational power, which restrict the use of advanced computer vision techniques. This work presents a lightweight and robust YOLOv8 (LR-YOLOv8) model designed for consumer electronics applications, bridging the gap between advanced computer vision algorithms and real-time operations on resource-constrained UAVs. The model integrates a lightweight architecture, a soft thresholding strategy to handle noisy data, and hue, saturation, and value (HSV) augmentation to simulate complex environments. Additionally, a multiscale domain-adapted loss function improves the mean average precision (mAP) and robustness. The experimental results demonstrate that, compared with the existing object detection methods, LR-YOLOv8 reduces the model weight, and increases the number of frames per second (FPS), while achieving a higher mAP.

Voltage regulation in distribution networks encounters a challenge of handling uncertainties caused by the high penetration of photovoltaics (PV). This research proposes an active exploration (AE) method based on reinforcement learning (RL) to respond to the uncertainties by regulating the voltage of a distribution network with battery energy storage systems (BESS). The proposed method integrates engineering knowledge to accelerate the training process of RL. The engineering knowledge is the chance-constrained optimization. We formulate the problem in a chance-constrained optimization with a linear load flow approximation. The optimization results are used to guide the action selection of the exploration for improving training efficiency and reducing the conserveness characteristic. The comparison of methods focuses on how BESSs are used, training efficiency, and robustness under varying uncertainties and BESS sizes. We implement the proposed algorithm, a chance-constrained optimization, and a traditional Q-learning in the IEEE 13 Node Test Feeder. Our evaluation shows that the proposed AE method has a better response to the training efficiency compared to traditional Q-learning. Meanwhile, the proposed method has advantages in BESS usage in conserveness compared to the chance-constrained optimization.