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Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

Abstract Homogeneous charge compression ignition (HCCI) combustion is a spontaneous multi-site auto-ignition of a lean premixed fuel–air mixture, which has high heat release rate, short combustion duration and no evidence of flame propagation. In HCCI engines, there is no direct control method for the time of auto-ignition. Auto-ignition timing should be controlled in order to make heat release process take place at the appropriate time in the engine cycle. Heat release analysis is a diagnostic tool which aids engine experimenters. It facilitates the endeavors being conducted in obtaining a control method by investigating heat release rate and also cumulative heat release. This study can be divided into two parts. First, traditional first law heat release model which is widely used in engine combustion analysis was presented and the applicability of this model in HCCI engines was investigated. Second, a new heat release model based on the first law of thermodynamics accompanying with a temperature solver was developed and assessed. The model was applied in four test conditions with different operating conditions and a variety of fuel compositions, including i -octane, n -heptane, pure NG, and at last, a dual fueled case of NG and n -heptane. Results of this work indicate that utilizing the modified first law heat release model together with a solver for temperature correction will guarantee obtaining a well-behaved and accurate apparent heat release trend and magnitude in HCCI combustion engines.

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

Current smart power grids have open communication infrastructures to improve efficiency, reliability and sustainability of supply. However, their open communication architecture makes them vulnerable to cyber-attacks with potentially catastrophic consequences. Here for the first time, we propose a time-delay-switch (TDS) attack by introducing time delays in the dynamics of power systems. Such an attack will have devastating consequences on smart grids if no prevention measures are considered in the design of these power systems. We considered how a TDS attack affects the dynamic performance of a power system. To do this, we first formulated a state space model of a smart power grid system under TDS attack using a hybrid systems approach. Second, we prove by analysis and demonstrate by simulation examples how a TDS attack can be used to sabotage and destabilize a smart grid.

Proton NMR imaging was used to investigate in situ the distribution of water in a polymer electrolyte membrane fuel cell operating on H2 and O2. In a single experiment, water was monitored in the gas flow channels, the membrane electrode assembly, and in the membrane surrounding the catalysts. Radial gradient diffusion removes water from the catalysts into the surrounding membrane. This research demonstrates the strength of 1H NMR microscopy as an aid for designing fuel cells to optimize water management.

We summarize the feasibility of using geothermal energy from the Western Canada Sedimentary Basin (WCSB) to support communities with populations >3000 people, including those in northeastern British Columbia, southwestern part of Northwest Territories (NWT), southern Saskatchewan, and southeastern Manitoba, along with previously studied communities in Alberta. The geothermal energy potential of the WCSB is largely determined by the basin’s geometry; the sediments start at 0 m thickness adjacent to the Canadian shield in the east and thicken to >6 km to the west, and over 3 km in the Williston sub-basin to the south. Direct heat use is most promising in the western and southern parts of the WCSB where sediment thickness exceeds 2–3 km. Geothermal potential is also dependent on the local geothermal gradient. Aquifers suitable for heating systems occur in western-northwestern Alberta, northeastern British Columbia, and southwestern Saskatchewan. Electrical power production is limited to the deepest parts of the WCSB, where aquifers >120 °C and fluid production rates >80 kg/s occur (southwestern Northwest Territories, northwestern Alberta, northeastern British Columbia, and southeastern Saskatchewan. For the western regions with the thickest sediments, the foreland basin east of the Rocky Mountains, estimates indicate that geothermal power up to 2 MWel. (electrical), and up to 10 times higher for heating in MWth. (thermal), are possible.

This paper presents a probabilistic harmonic simulation method to study the power-quality impact of electric vehicles (EVs). The proposed method takes into account the random operating characteristics of the vehicles, such as random charging time, charging duration, and vehicle locations. A multiphase model of the primary and secondary systems is used to represent the study system. Extensive case studies have been conducted using the method and the measured EV data. The results reveal that the current versions of plug-in hybrid EVs have no significant harmonic impact on power systems. However, the Level 1 charger can increase the neutral-to-earth voltage at homes which could lead to increased stray voltage incidents.

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Abstract We investigate the impacts of capacity payment mechanisms on generation capacity investment and subsequent energy and capacity market competition. We analyze the optimal design of capacity demand parameters. The presence of price-responsive capacity demand reduces firms' abilities to exercise market power, alleviates the volatile bimodal capacity market pricing structure, reduces market concentration, and increases expected consumer surplus. While capacity payments expand investment, the first-best aggregate level of capacity is not achieved at the optimal capacity payment parameters. We demonstrate that the addition of subsidized renewable generation capacity puts downward pressure on energy prices. When renewable output is not sufficiently positively correlated with energy demand, renewable capacity expansion reduces the expected available production capacity and increases the probability of capacity scarcity for a given level of capacity demand parameters. Consequently, the regulator must expand capacity payments to achieve the same level of resource adequacy.