Efficient Energy Management of a Low-Voltage AC Microgrid with Renewable and Energy Storage Integration Using Nonlinear Control

This paper proposes an enhanced nonlinear control strategy combined with efficient energy flow management for a low-voltage AC microgrid integrating a wind turbine, a photovoltaic system, and a battery energy storage unit. The microgrid operates in a grid-connected configuration, aiming to optimize energy generation, storage, and consumption. To achieve this, a comprehensive mathematical model of the system is developed, and backstepping controllers are designed to fulfill the control objectives. The stability of the closed-loop system is rigorously verified using Lyapunov theory. Furthermore, a novel algorithm is introduced to maximize renewable energy extraction while effectively managing battery storage to enhance system …

Research on Improved Cable-Length-Adaptive Sensorless Vector Control for Drives of Induction Motors in Deep-Sea Oilfields

This paper addresses the issue of the insufficient adaptability of existing vector control algorithms to filter and compensate for long cable parameters (under the influence of parasitic impedance, parasitic capacitive reactance, and parasitic inductive reactance), which leads to unresolved stability and harmonic issues in long-cable drives for deep-sea oil fields.…

Unveiling and Estimating Behind-the-Meter Rooftop Solar Self-Consumption Using Explainable AI

As renewable energy adoption grows, rooftop solar for self-consumption is also increasing. This “behind-the-meter” self-consumption is usually unmeasured, making grid operation difficult. However, few studies have estimated self-consumption. This study analyzes the impact of weather and proposes a framework for estimating self-consumption at grid using readily available data. Employing machine learning with XAI, the downward impact of solar radiation on grid demand is quantified to estimate self-consumption. Using actual Australia data, self-consumption was estimated under different solar adoption levels, demonstrating the high accuracy and versatility. Additionally, the increase in self-consumption in summer was quantified by the interaction between temperature and …

A Hierarchical Dataset on Multiple Energy Consumption and PV Generation with Emissions and Weather Information

This study constructs a multi-source and hierarchical dataset of energy consumption, photovoltaic (PV) power generation, greenhouse gas (GHG) emissions, and weather information, dubbed Hierarchical Energy, Emissions, and Weather (HEEW). This dataset contains 11,987,328 records for 147 individual buildings, four aggregated communities, and the entire region, which is structured as time-series tables indexed by building ID and timestamps from 1 January 2014 to 31 December 2022. It includes 13 hourly variables as follows. Energy records involve PV output and total energy consumption of electricity, heat, and cooling loads. Weather involves temperature, dew point, humidity, wind speed, wind gust, pressure, and precipitation. …

Positive Sequence Reactive Current Differential Protection of Transmission Lines Connected to Energy Storage Power Station

When the energy storage power station encounters a fault on the transmission line during charging, active component of its short-circuit current still maintains an inverse relationship with the positive-sequence voltage at its grid connection point, influenced by the converter control strategy. This leads to a large phase difference between the short-circuit currents on both sides of the transmission line, posing a risk of no-trip failure in conventional current differential protection. To address the above issues, this paper proposes a differential protection scheme for transmission line connected to energy storage power stations based on positive-sequence reactive current, which can effectively avoid …

Optimizing Carbon Footprint in Long-Haul Heavy-Duty E-Truck Transportation

Electrifying heavy-duty trucks is crucial for decarbonizing transportation, but maximizing their potential requires minimizing the carbon footprint of timely deliveries. This complex optimization task involves strategic path, speed, and charging planning, which traditional methods struggle to optimize at scale. We present a stage-expanded graph formulation that reduces complexity and reveals a useful problem structure. Our formulation naturally decomposes the problem into more tractable subproblems, allowing efficient coordination between routing and charging decisions, and maintains a manageable graph size. We exploit these structural insights to design an efficient algorithm with performance guarantees. Simulations using real-world data over the U.S. highway system …

Recycling: A Key Enabler for Sustainable Nuclear Fuel Cycles

As the global nuclear energy landscape evolves, there is a compelling strategic case for revitalising approaches to used fuel recycling.

Recycling enables the recovery and reuse of valuable materials from used fuel, reducing the demand for newly mined uranium, lowering the volume and radiotoxicity of high-level waste, and strengthening the resilience of global supply chains amid rising energy needs. It also supports the development of the next generation of nuclear reactor technologies, many of which are specifically designed to operate on recycled fuel. Moreover, recycling aligns with broader circular economy principles and offers the nuclear industry an opportunity to shift …

Comparing the Climate and Air Pollution Footprints of Lithium-Ion BEVs and ICEs in the US Incorporating Systemic Energy System Responses

With rising travel demand and the need to tackle both the air quality and climate change challenges caused by fossil fuel vehicles, there is an urgent need to transition to cleaner and more sustainable fuels. While lithium-ion battery electric vehicles (BEVs) produce no emissions during operation, they increase electricity consumption, affecting emissions from that activity. Furthermore, there is an ongoing debate about the overall cleanliness of lithium-ion batteries when assessing emissions throughout their lifecycle compared to fossil fuels. To address these concerns, we use the Global Change Analysis Model (GCAM) integrated assessment model (IAM) to evaluate criteria air pollutants and …

Renewable Energy Consumption Optimization Allocation Strategy for a Regional Microgrid Based on a Shared Energy Storage Mechanism

With the continuous maturation of the sharing economy model, the shared energy storage station service model emerges as a promising user-side energy storage application. This article proposes a bilayer optimal configuration method for regional microgrid systems, leveraging shared energy storage station services. First, this article introduces and analyzes the new model’s operation mechanism and profit model (the financial framework that outlines how the shared energy storage station service generates revenue and manages costs to achieve profitability), emphasizing its role in energy sharing governance to enhance economic and operational efficiency within the microgrid system. Second, this service model was applied to …

Assessing Mental Health in Individuals Near Thermal Power Plants and Development of Depression Predictive Model

Depression, anxiety, and stress are major mental health concerns globally, especially in India. This study examines the prevalence of mental health symptoms in overweight and normal BMI individuals living near thermal power plants and develops a depression prediction model using binary logistic regression using the DASS-21 score. A community-based cross-sectional study was conducted from October 2018 to March 2019, with data collected through face-to-face interviews. Socio-demographic factors like age, gender, cooking fuel type, and income were analyzed. Significant associations were found between stress and household air pollution (p = 0.011, OR = 17.408, 95% CI) and between anxiety and income …