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Emergency Medical Services (EMS) are vital for providing timely out‐of‐hospital care during medical emergencies. This research aims to optimize ambulance services by strategically allocating resources to minimize response time. A modified Whale Optimization Algorithm (mWOA) is introduced to achieve this goal, focusing on providing 24 × 7 services to every patient in need. The, conducted in Southern Delhi, India, considers the uncertain and stochastic nature of demand and traffic. The results demonstrate a 14.6% improvement in average EMS‐based response time, highlighting the effectiveness of the mWOA algorithm in enhancing ambulance allocation strategies. The results obtained using different algorithms are compared with those obtained using mWOA. The experiment outcomes demonstrate that the mWOA has higher efficiency and superiority than alternative algorithms regarding convergence rate and stability. [ABSTRACT FROM AUTHOR]

The abundance of powered semiconductor devices has increased with the introduction of renewable energy sources into the grid, causing power quality disturbances (PQDs). This represents a huge challenge for grid reliability and smart city infrastructures. Accurate detection and classification are important for grid reliability and consumers' appliances in a smart city environment. Conventionally, power quality monitoring relies on trivial machine learning classifiers or signal processing methods. However, recent advancements have introduced Deep Convolution Neural Networks (DCNNs) as promising methods for the detection and classification of PQDs. These techniques have the potential to demonstrate high classification accuracy, making them a more appropriate choice for real-time operations in a smart city framework. This paper presents a voting ensemble approach to classify sixteen PQDs, using the DCNN architecture through transfer learning. In this process, continuous wavelet transform (CWT) is employed to convert one-dimensional (1-D) PQD signals into time–frequency images. Four pre-trained DCNN architectures, i.e., Residual Network-50 (ResNet-50), Visual Geometry Group-16 (VGG-16), AlexNet and SqeezeNet are trained and implemented in MATLAB, using images of four datasets, i.e., without noise, 20 dB noise, 30 dB noise and random noise. Additionally, we also tested the performance of ResNet-50 with a squeeze-and-excitation (SE) mechanism. It was observed that ResNet-50 with the SE mechanism has a better classification accuracy; however, it causes computational overheads. The classification performance is enhanced by using the voting ensemble model. The results indicate that the proposed scheme improved the accuracy (99.98%), precision (99.97%), recall (99.80%) and F1-score (99.85%). As an outcome of this work, it is demonstrated that ResNet-50 with the SE mechanism is a viable choice as a single classification model, while an ensemble approach further increases the generalized performance for PQD classification. [ABSTRACT FROM AUTHOR]

With the promotion and development of clean energy, it is challenging to ensure the optimization of control performance in frequency control of the hydropower-photovoltaic hybrid microgrid system caused by the output power fluctuation of photovoltaic power generation. In this study, an optimal load frequency controller (LFC) for a hydropower-photovoltaic hybrid microgrid system was designed to improve the dynamic response when the load and photovoltaic output power are perturbed based on the off-policy integral reinforcement learning algorithm. First, a mechanism model of the hydropower-photovoltaic hybrid microgrid system was established. Next, the LFC problem was transformed into a zero-sum game control problem based on the characteristics of the power system. Subsequently, three neural networks were employed to approximate the Nash equilibrium solution of the zero-sum game with historical input and output data when the system dynamics are completely unknown. Finally, simulation experiments were conducted to verify the effectiveness and optimality of the proposed method. The introduction of this method provides a new perspective for frequency control for the hydropower-photovoltaic hybrid microgrid system. [ABSTRACT FROM AUTHOR]

This article discusses a support vector regression-based interval power flow prediction method for distribution networks with distributed generators (DGs) integration. The method aims to address the challenges of uncertain power flow analysis in distribution networks, particularly as the size of the system increases. The proposed method utilizes intervals to describe system uncertainty and employs support vector regression for model training. Simulation results demonstrate that the method exhibits high prediction accuracy, adaptability, and computation efficiency, meeting the requirements for rapid and real-time power flow analysis in distribution networks with DGs integration. The text also discusses the construction of an Interval Power Flow (IPF) prediction model for distribution networks using Support Vector Regression (SVR). The model is a multi-output model that predicts the upper and lower bounds of power flow results, taking into account uncertainties in the distribution system. The model has been shown to have high prediction accuracy and computational efficiency in studies conducted on IEEE 33bw and IEEE 69 cases. The text concludes by stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The article titled "Distributionally robust multistage dispatch with discrete recourse of energy storage systems" by Zheng et al. explores the concept of distributionally robust multistage dispatch with discrete recourse of energy storage systems. The authors propose a mathematical model that considers uncertainties in renewable energy generation and electricity demand, and aims to optimize the dispatch of energy storage systems. The study highlights the importance of incorporating uncertainty [Extracted from the article]

Profit maximization is crucial for both producers and customers in power grids, particularly in deregulated energy markets. By focusing on profit maximization measures, power grids can improve economic viability and sustainability for all stakeholders. The growing demand for electricity in modern civilization poses a significant challenge to the power grid's ability to meet it. To address these difficulties, the power industry is attempting to integrate renewable sources into existing power facilities. Because of its unpredictable character, the presence of renewable energy in the electricity system becomes increasingly important and adds complexity to the grid. Given the intermittent nature of renewable sources, an energy storage device is required in this scenario. During the low-power use phase, the grid has ample power. Meanwhile, energy storage devices can be utilized to store extra power in the low power demand phase while maintaining the safety and stability of the power network during peak demand periods. V2G (Vehicle-to-Grid) technology is commonly employed to address the uncertainty of renewable resources while maintaining system stability. By using automobiles as transportable storage devices, V2G can make the best use of excess electricity during low-energy hours. Wind energy's volatility causes imbalances, lowering system profitability. With wind energy integration, power system profitability can be improved by analyzing uncertainties and employing mitigation strategies. To overcome the issue, the research optimizes the use of V2G and TCSC (thyristor-controlled series compensator). V2G permits bidirectional power flow, lowering imbalance pricing (IP) while considering power demand. TCSC can increase the transient stability of transmission lines, hence reducing line faults. TCSC's involvement in the system increases profitability by lowering congestion expenses. Strategic TCSC placement increases overall profitability. The approach strives to offer efficient, environmentally friendly power at a reasonable price while addressing the needs of both power providers and customers. This study also looks into the effects of V2G mixing on voltage stability, LMP (locational marginal price), and the effectiveness of a TCSC in a deregulated electrical system. MiPower software is used to simulate operation and find the optimal placement of the storage unit within the IEEE 14-bus system. [ABSTRACT FROM AUTHOR]

Introduction: The integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging. Methods: To address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis. Results: The proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (N PC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case. Discussion: Seasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption. [ABSTRACT FROM AUTHOR]