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This paper presents a systematic review to align current and future research in smart grids (SG) and smart urban energy systems (SUES) and unify the diverse but fragmented research in urban energy management. First, the common research themes repeatedly appearing in the SG and SUES literature are categorized into logical physical, cyber, and social energy processes. Critical findings are summarized for each part, i.e., the advances and trends in physical energy generation, transmission, consumption, and storage processes of cities for energy interventions, the cyber process chain of energy sensing, modeling, forecasting, scheduling, coordinating, and adapting for enhancing the "smartness" of power and urban energy systems, and the social energy processes of understanding, protecting, engaging, and governing human to be a new smartness dimension. Second, the process-based framework provides insights on the design and implementation of SG and SUES for engineers, planners, and policymakers, such as optimizing the system performance by upgrading individual processes and exploring new links among identified physical, cyber, and social energy processes. The upscaling barriers and strategies of sustainable energy technologies in the development and deployment stages are also summarized to scale up sustainable energy processes to a higher level. Finally, future research directions are envisioned, including developing data markets, privacy-preserving data sharing and analytics for urban-scale energy modeling, refining policy and standards for safety, privacy, cybersecurity, and interoperability issues, assessing technology maturity and energy system success factors, and removing regulatory and structural barriers. [Display omitted] • A systematic review intersecting smart grid and smart urban energy systems is done. • A holistic multi-scale multi-process cyber-physical-social framework is developed. • Advances and trends in physical/cyber/social energy processes are reviewed. • Sustainable upgradation, integration, and scale-up of energy processes are suggested. • Guidelines and future directions are given for smart grid and urban energy systems. [ABSTRACT FROM AUTHOR]

Ti-doped CoSb 3 thin films were prepared through layer inter-diffusion method and the prepared thin films have unique architecture, containing nano-size grains. According to the Raman analysis, the films have weaker atomic vibration after Ti doping. This nano-structure and the weak atomic vibration can scatter a wide-range spectrum of phonons, resulting the significantly decrease of thermal conductivity. In addition, this material design also leads to a very important improvement of the absolute Seeback coefficient value, which increases from about 50 μVK−1 to over 100 μVK-1. As the result, the maximum ZT value is 0.86 at 523 K for a Ti doped sample, which is six times higher than the un-doped sample and is comparable to the best value for the CoSb 3 based thin films. A flexible CoSb 3 based thin film thermoelectric generator was also fabricated and shows that the response time of all the touch events is below 1 s. [ABSTRACT FROM AUTHOR]

• A parallel computational building-chain (PCBC) model is developed for rapid urban-scale building energy modeling (UBEM). • The PCBC model is compared with the Whole City model on virtual and actual buildings. • Via parallel computation, PCBC achieves the same outputs much more quickly. • The advantages of the PCBC method increase with the problem scale. As buildings consume substantial amounts of energy, researchers and decision makers are giving more attention to urban-scale energy assessment and design. In fact, researchers have developed many building-energy modeling and simulation tools that are regarded as effective for building designers and facility managers. However, few models have been found suitable for urban-scale efficient building design and planning. Indeed, to carry out urban-scale energy modeling and simulation, it is necessary to possess a comprehensive understanding of interactions among building groups as well as huge computational resources. To develop a more efficient and reliable simulation model, this study proposes a parallel computational building-chain (PCBC) model. This PCBC model aims to simplify building interactions and implement efficient multi-thread computations. It can decompose large-scale building groups into inter-connected building units by defining the thermal and shading boundary conditions of buildings in a neighborhood. By coupling individual buildings' simulated energy consumption, the urban energy dynamics can be reconstructed. To validate the proposed method, researchers examined a sample urban-building group with 410 buildings. Compared with the conventional integrated Whole City model, the proposed method achieved nearly the same outputs with reduced computation time. With an increase in the simulation scale, computational efficiency can be improved in the future. [ABSTRACT FROM AUTHOR]

• This work revealed the law of gas sulfides release during the reduction process of blast furnace raw materials. • The precipitation of sulfur during the reaction of coke and sinter in reducing atmosphere was divided into two stages. • Low temperatures favored the generation of H 2 S, while high temperatures promoted the generation of COS. Understanding the transformation behavior of sulfides in the blast furnace raw materials during the ironmaking process is crucial for controlling the form and emission of gaseous sulfides in the blast furnace top gas. This study investigates the reactions of coke and sinter at different reducing atmosphere ranging from 200 °C to 1000 °C by using of thermogravimetry and mass spectrometry (TG-MS). Additionally, thermodynamic theoretical calculations were performed to enhance the understanding of the reaction behavior. The results showed that the mixed sinter and coke started to generate H 2 S at 100 °C under the mixed H 2 -CO atmosphere and reached the peak between 350 and 400 °C. With the increase of H 2 concentration, the peak temperature of H 2 S generation initially decreased and then increased, with the lowest peak temperature observed under a 50% H 2 -50% CO atmosphere. Furthermore, COS generation started around 300 °C and reached its peak at approximately 800 °C. The peak content of COS generation increased as the CO concentration. Low temperatures favored the generation of H 2 S from the mixed sinter and coke, while high temperatures were conducive to the generation of COS. [ABSTRACT FROM AUTHOR]

• A bottom-up approach is proposed for household short-term load forecasts. • Forecast granularity is analyzed on the appliance, room, and household levels. • Results demonstrate the promise of the bottom-up approach for accurate house-load forecasts. • A recontextualized Kalman filter model is applied for appliance usage forecast. • The bottom-up approach with Kalman filter models reduces errors by half. Renewable energy sources are now being used with buildings like PV panels. Consequently, short-term household load forecast plays an important role in managing distributed energy generation, local consumption, and grid-building integration. Forecasting household load, however, can be an intractable problem. These loads are characterized by large uncertainty and variations, leaving much room to improve accuracy. To improve the household load forecast accuracy, this paper advocates a Kalman filter-based bottom-up approach. First, using a deep learning model and a persistence model on public datasets, the authors verified the advantage of the bottom-up approach through granularity analysis at the appliance, room, house levels. Employing the Symmetric Mean Absolute Percentage Error, the authors compared two strategies: (1) the conventional strategy, which forecasts the load directly at the household level, and (2) the bottom-up strategy, which aggregates the forecasts made at the room or appliance level. Experimental results on public datasets demonstrated that the bottom-up approach holds great promise. Second, as the bottom-up approach is often criticized for the cost, the authors designed a recontextualized Kalman filter model to efficiently forecast appliance energy usages. Using two strategies, the authors compared the Kalman filter-based bottom-up approach with deep-learning models. They found the bottom-up approach reduced forecast errors 49% more than the deep-learning models and 47% more than the conventional strategy. Finally, the authors concluded that a Kalman filter-based bottom-up approach could efficiently improve household load forecast accuracy. The findings could help give fast and accurate load forecasts for building energy management and predictive controls. [ABSTRACT FROM AUTHOR]

Abstract The growing interest in green building is driving the development of energy efficiency building models. One of the essential elements is an effective and efficient Energy Management system. Traditionally, the system contains lots of meters measuring each appliance. Non-Intrusive Load Monitoring (NILM) is a technique for estimating power consumption of each appliance by disaggregating the end-use power consumption measured by a single meter. However, existing NILM are facing two main problems: 1) some of the small appliances and the similar appliances cannot have their power consumption estimated, and 2) NILM requires a long learning process, which means that a new learning process might be needed if a new appliance is connected. To tackle these problems, this paper proposes to use Intrusive Signature (IS) and Power Line Communication (PLC) for circuit-level load decomposition. The paper focuses on the development of a device using frequency domain signature as the IS. The prototype is developed and deployed on the circuit. The results show an accuracy between 83% to 93%. [ABSTRACT FROM AUTHOR]

Abstract The building sector takes a large proportion of electricity consumption and carbon emission in high-density urban areas. To reduce the carbon emissions and use energy more efficiently in the building sector, home energy management system (HEMS) is proposed and used. In the HEMS, the prediction of electricity consumption in the short-term future is used to support the decision makings in the HEMS. Although there existed a number of studies in the prediction of electricity consumption in buildings, there lacks a spatial analysis in the prediction performance, especially on the appliance or sub-meter level and household level. The authors made an assumption that by the performance of household energy consumption prediction can be significantly improved if the prediction is aggregated from the prediction data at the appliance or sub-meter level. Next, two typical datasets are used to validate the assumption by comparing the prediction performance of aggregating the prediction data at appliance level and the one of making direct prediction at the household level. The models used for the prediction are standard stateful long short-term memory (LSTM) neural networks, which have been proofed to be promising in load prediction by previous studies. The results from the comparison validated the assumption, showing that the prediction performance can be significantly improved if prediction is made at the appliance-level first and then aggregated to get the household-level prediction. Therefore, the authors conclude that prediction at the finer appliance granularity level can significantly improve the performance of household-level electricity prediction. [ABSTRACT FROM AUTHOR]

An easy method of preparing well-crystallized Sb 2 Se 3 films and nanorods through magnetron sputtering was proposed, and their growth mechanism was examined. The microstructure, morphology, composition, and optical and electrical properties of the Sb 2 Se 3 films depended strongly on the substrate temperature ( T sub ). Sb 2 Se 3 films prepared at > 250 °C contained the orthorhombic phase without Sb 2 O 3 phase owing to the high-purity vacuum environment. Well-crystallized Sb 2 Se 3 films deposited at T sub = 325 °C showed compact grains with a bandgap of 1.23 eV and a high absorption coefficient of 10 5 cm −1 in the visible region. Photoelectrochemical measurements showed that Sb 2 Se 3 films were p-type semiconductors with excellent photoresponse. A clear photovoltaic effect with a power conversion efficiency (PCE) of 3.35% was measured for the first time by using the sputtering Sb 2 Se 3 films as absorbers in a photovoltaic solar cell. At T sub = 375 °C, highly uniform films with monodispersed Sb 2 Se 3 nanorods were obtained. The preferential orientation of the Sb 2 Se 3 crystals by self-organization was promoted by increasing T sub . By using the Sb 2 Se 3 nanorods as absorbers in a solar cell, good photoresponse and a PCE of 2.15% were achieved. [ABSTRACT FROM AUTHOR]

A drastic improvement in the power factor of Al-doped ZnO (AZO) thin film was achieved by introducing Ti as a co-dopant with Al. A Ti super-thin buffer layer was firstly deposited on the glass substrate, and then AZO thin films were deposited onto Ti buffer layer at room-temperature. The introduction of Ti buffer layer facilitated the growth of AZO films and improved its crystallinity. XPS and XRD results showed that Ti 4+ substituted for Zn 2+ site, which created extra electrons. Dual doping of ZnO with Al and Ti drastically increased the electrical conductivity and the Seebeck coefficient of AZO films at high temperature. As a result, a significant enhanced power factor as high as 12.8 × 10 −4 Wm −1 K −2 was achieved at 573 K in Ti co-doped AZO films, which was more than 18 times larger than that of the films without Ti co-doping (0.64 × 10 −4 Wm −1 K −2 ). [ABSTRACT FROM AUTHOR]

The skutterudite compound CoSb 3 exhibits excellent thermoelectric properties for thermoelectric application. The composition plays an important role in affecting the microstructural and thermoelectric behavior of CoSb 3 , especially in the form of thin film. In this study, the Co-excess and Sb-excess CoSb 3 thin films were prepared by radio frequency and direct magnetron co-sputtering technique. It was found that the thin film with excess Co exhibits a significant n-type conduction behavior and transforms to p-type conduction behavior with Sb rich. The X-ray diffraction patterns show that either the Co-excess or Sb-excess samples contain skutterudite CoSb 3 and a secondary phase CoSb 2 . Additionally, Sb can be found in the Sb-excess samples, yet no Co can be observed in the Co-excess samples. [ABSTRACT FROM AUTHOR]

Here we report the In doped ZnSb thermoelectric thin films which were deposited by direct current magnetron co-sputtering with prefabricate layer doping method. The X-ray diffraction result indicates that the peaks of the In doped ZnSb thin films are related to ZnSb phase and are slightly shifted to smaller angle. The calculations of In occupy one of the Sb or Zn location in normal site were performed based on the first-principles and it has lower total energy when the In substitutes the Zn. The thermo-electrical testing experimental results indicate that the Seebeck coefficient increases greatly after In doped due to increase of the total density of states and the complicate of the electronic band structure. The Fermi surface moves to the valence band after In doped that will reduce the electrical conductivity and it corresponds to the testing result. It can be observed that the power factor of the In doped ZnSb is approach two times than that of the thin film without doping. [ABSTRACT FROM AUTHOR]

• A bottom-up intra-hour proactive scheduling for household peak avoiding. • Physic-based modeling and simulation of thermal appliances. • MPC controller formulation with soft constraints and flat tariffs. • Minimization of high-power appliances' concurrent operations considering user comfort. • Key factors and computational complexity analyzed. Residential-level peak shaving is beneficial for the supply–demand balance of buildings with a limited power capacity of renewable energy sources like building-integrated photovoltaics. Thermostatically controlled loads (TCLs) are recognized as flexible resources for peak shaving. In practice, most thermal appliances are controlled by local on–off controllers without any communication and coordination. Multiple appliances may operate simultaneously and produce load peaks. Existing studies assume the existence of an electricity market and time-varying tariffs, which are not always available. This paper developed a model predictive control (MPC) based bottom-up proactive method to leverage the thermal inertia of residential thermal appliances for avoiding household peak load without relying on time-varying tariffs. First, we used a physics-based modeling approach to model the thermal dynamics of several residential thermal devices. Then, we designed a centralized MPC controller to minimize the appliance concurrent operations considering occupant comfort zones and appliance rate powers. Consequently, the house peak load is reduced by minimizing concurrent operations. Simulations over homogenous and heterogenous appliance set validated that the MPC-based proactive scheduling method can effectively reduce household peak load without compromising user comfort too much. Finally, we discussed the impact of control interval, appliance rated powers, and penalty factors on peak avoiding and the computational feasibility of the MPC controller. We concluded that the MPC-based proactive scheduling is feasible and effective for a limited number of thermal appliances with short horizons (i.e., six-step). Replacing a mixed-integer nonlinear program solver with a mixed-integer quadratic program solver could significantly reduce the computational burden. However, the application of the MPC controller to multiple households at the system level is still impractical. Further research efforts are emphasized, like mining and forecasting occupant demand and appliance usage patterns in households and designing scalable control strategies for system-level peak shaving. [ABSTRACT FROM AUTHOR]

Measurements of electronic structure, microstructure and thermoelectric properties of zinc antimonide thin films prepared by direct current magnetron co-sputtering were carried out. The as-deposited zinc antimonide thin film had a very high resistivity similar to insulating ceramics, which was due to a low binding energy of both zinc and antimony, with the electron scattering increases and impedes the current transport. With the increase in annealing temperature, the films became more crystalline and the thermoelectric properties were also improved. The resistivity of the film decreased rapidly with its crystallinity when the annealing temperature was above 350 °C. The Seebeck coefficients of the thin films were positive, indicating that the films were P-type. The Seebeck coefficient of those samples increased with increasing annealing temperature. The thin film annealed at 400 °C has an optimal power factor of 1.87 × 10 −3 Wm −1 K −2 with a Seebeck coefficient of 300 μVK −1 and a resistivity of 4.82 × 10 −5 Ωm at 573 K. [ABSTRACT FROM AUTHOR]

Antimonide cobalt thin films were deposited on BK7 glass substrates at various substrate temperatures by ion beam sputtering deposition with a fan-shape target. The influence of deposition temperature on the microstructure and thermoelectric properties of antimonide cobalt thin films were systematically investigated. It is found that the Seebeck coefficient of the thin film increases at first and then decreases with the increasing deposition temperature. The Seebeck coefficient of the sample deposited at 250 °C has maximum value and increases stably when the measuring temperature increased from room-temperature to 600 K. The electrical conductivity of the thin film increases significantly to 5.6 × 10 4 S cm −1 when the deposition temperature was 450 °C and then decreases greatly when the temperature increased to 500 °C and 550 °C. The behavior of electrical conductivity of the sample deposited at 250 °C changes from metallic to semiconducting after the measuring temperature exceeded 540 K. The power factor of antimonide cobalt thin film deposited at 250 °C has a maximum value of 0.93 × 10 −4 W m −1 K −2 at room-temperature and then increases to 3.5 × 10 −4 W m −1 K −2 when the measuring temperature was 540 K. [ABSTRACT FROM AUTHOR]

Highlights: [•] Zn-Sb thin film thermoelectric property enhanced via phase optimization. [•] The carrier concentration is suitable for obtaining high-quality sample. [•] Zn-Sb thin film with mixed ZnSb and Zn4Sb3 phase has highest power factor. [ABSTRACT FROM AUTHOR]