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There are many real-world applications that require high-performance mobile computing systems for onboard, real-time processing of gathered data due to latency, reliability, security, or other application constraints. Unfortunately, most existing high-performance mobile computing systems require a prohibitively high power consumption in the face of the limited power available from the batteries typically used in these applications. For high-performance mobile computing to be practical, alternative hardware designs are needed to increase the computing performance while minimizing the required power consumption. This article surveys the state-of-the-art in high-efficiency, high-performance onboard mobile computing, focusing on the latest developments. It was found that more research is needed to design high-performance mobile computing systems while minimizing the required power consumption to meet the needs of these applications.

Electricity imbalance pricing provides the ultimate incentive for generators and suppliers to contract with one another ahead of time and deliver against their obligations. As delivery time approaches, traders must judge whether to trade-out a position or settle it in the balancing market at the as-yet-unknown imbalance price. Forecasting the imbalance price (and related volumes) is therefore a necessity in short-term markets. However, this topic has received surprisingly little attention in the academic literature despite clear need by practitioners. Furthermore, the emergence of algorithmic trading demands automated forecasting and decision-making, with those best able to extract predictive information from available data gaining a competitive advantage. Here we present the case for developing imbalance price forecasting methods and provide motivating examples from the Great Britain’s balancing market, demonstrating forecast skill and value.

Fracture conductivity decline is a concern in the Tuscaloosa Marine Shale (TMS) wells due to the high content of clay in the shale. An analytical well productivity model was developed in this study considering the pressure-dependent conductivity of hydraulic fractures. The log-log diagnostic approach was used to identify the boundary-dominated flow regime rather than the linear flow regime. Case studies of seven TMS wells indicated that the proposed model allows approximation of the field data with good accuracy. Production data analyses with the model revealed that the pressure-dependent fracture conductivity in the TMS in the Mississippi section declines following a logarithmic mode, with dimensionless coefficient χ varying between 0.116 and 0.130. The pressure-dependent decline of fracture conductivity in the transient flow period is more significant than that in the boundary-dominated flow period.

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China’s renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t.

Achieving the global electricity demand and meeting the United Nations sustainable development target on reliable and sustainable energy supply by 2050 are crucial. Portable energy storage (PES) units, powered by solid-state battery cells, can offer a sustainable and cost-effective solution for regions with limited power-grid access. However, operating in high-dust and high-temperature environments presents challenges that require effective thermal management solutions. This paper is a comprehensive review of thermal management systems for PES units, with a specific focus on addressing the challenge of overheating in airtight designs. The review of various active and passive cooling systems is conducted through extensive study of the relevant literature, which is significant in providing insights into the operation, performance parameters, and design options for different cooling system technologies. The findings from this review show heat pipe (HP) technologies as key cooling-system solutions for airtight PES units. Specifically, loop and oscillating HPs, as well as the vapour chamber, offer desirable features such as compactness, low cost, and high thermal conductivity that make them superior to other alternatives for the cooling systems in PES. The insights and knowledge generated via this review will help facilitate the design and development of innovative, efficient, and reliable PES units, thereby contributing to the advancement of off-grid renewable energy applications and enabling sustainable power solutions worldwide. Furthermore, an appropriate design of PES units can help in reducing capital and maintenance costs.

This study proposes a very effective formulation to carry out the security-constrained direct current (DC)-based optimal power flow (OPF) problem using two linear factors: (i) the power transmission distribution factors (PTDF) and (ii) the line outage distribution factors (LODF). The security-constrained (SC) DCOPF problem has been reformulated using these linear distribution factors, and mainly the pre- and post-contingency constraints have been added into the optimization problem based on the active power unit generation (decision variables). The main advantage of this formulation is the reduction of decision variables as well as equality and inequality constraints. To validate the introduced formulation, several experiments have been conducted using MatPower, DIgSILENT Power Factory and Gurobi. Simulation results demonstrate both the feasibility to carry out the SCOPF problem and the potential applicability of the proposed formulation to medium and large-scale power systems.

Freshwater scarcity is a significant concern due to climate change in some regions of Brazil; likewise, evaporation rates have increased over the years. Floating photovoltaic systems can reduce water evaporation from reservoirs by suppressing the evaporating area on the water surface. This work evaluated the effects of floating photovoltaic systems on water evaporation rates in the Passaúna Reservoir, southeastern Brazil. Meteorological data such as temperature, humidity, wind speed, and solar radiation were used to estimate the rate of water evaporation using FAO Penman–Monteith, Linacre, Hargreaves–Samani, Rohwer, and Valiantzas methods. The methods were tested with the Kruskal–Wallis test, including measured evaporation from the nearest meteorological station to determine whether there were significant differences between the medians of the methods considering a 95% confidence level for hypothesis testing. All methods differed from the standard method recommended by the FAO Penman–Monteith. Simulations with more extensive coverage areas of the floating photovoltaic system were carried out to verify the relationship between the surface water coverage area and the evaporation reduction efficiency provided by the system and to obtain the avoided water evaporation volume. For the floating photovoltaic system with a coverage area of 1265.14 m2, an efficiency of 60.20% was obtained in reducing water evaporation; future expansions of the FPS were simulated with coverage areas corresponding to energy production capacities of 1 MWp, 2.5 MWp, and 5 MWp. The results indicated that for a floating photovoltaic system coverage area corresponding to 5 MWp of energy production capacity, the saved water volume would be enough to supply over 196 people for a year. More significant areas, such as covering up the entire available surface area of the Passaúna reservoir with a floating photovoltaic system, could save up to 2.69 hm3 of water volume annually, representing a more significant value for the public management of water resources.

The rapid increase in the penetration of photovoltaic (PV) power plants results in an increased risk of grid failure, primarily due to the intermittent nature of the plant. To overcome this problem, the flexible power point tracking (FPPT) algorithm has been proposed in the literature over the maximum power point tracking (MPPT) algorithm. These algorithms regulate the PV power to a certain value instead of continuously monitoring the maximum power point (MPP). The proposed work carries out a detailed comparative study of various constant power generation (CPG) control strategies. The control strategies are categorized in terms of current-, voltage-, and power-based tracking capabilities. The comparative analysis of various reported CPG/FPPT techniques was carried out. This analysis was based on some key performance indices, such as the type of control strategy, irradiance pattern, variation in G, region of operation, speed of tracking, steady-state power oscillations, drift severity scenario, partial shading scenario, implementation complexity, stability, fast dynamic response, robustness, reactive power, cost, and tracking efficiency. Among existing FPPT algorithms, model-based control has a superior performance in terms of tracking speed and low steady-state power oscillations, with a maximum tracking efficiency of 98.57%.