With the development of new technologies in the field of renewable energy and batteries, increasing number of houses have been equipped with renewable energy sources (RES) and energy storage systems (ESS) to reduce home energy cost. These houses usually have home energy management systems (HEMS) to control and schedule every electrical device. Various studies have been conducted on HEMS and optimization algorithms for energy cost and peak-to-average ratio (PAR) reduction. However, none of papers give a sufficient study on the utilization of main grid's electricity and selling electricity. In this paper, firstly, we propose a new HEMS architecture with RES and ESS where we take utilization of the electricity of the main grid and electricity selling into account. With the proposed HEMS, we build general mathematical formulas for energy cost and PAR during a day. We then optimize these formulas using both the particle swarm optimization (PSO) and the binary particle swarm optimization (BPSO). Results clearly show that, with our HEMS system, RES and ESS can help to drop home energy cost significantly to 19.7%, compared with the results of previous works. By increasing charge/discharge rate of ESS, energy cost can be decreased by 4.3% for 0.6 kW and 8.5% for 0.9 kW. Moreover, by using multi-objective optimization, our system can achieve better PAR with an acceptable energy cost. INDEX TERMS Home energy management systems, electricity selling, renewable energy sources, energy storage systems, day-ahead price, meta-heuristic algorithms.

In this study, we investigate the operation of an optimal home energy management system (HEMS) with integrated renewable energy system (RES) and energy storage system (ESS) supporting electricity selling functions. A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. The effect of different selling prices on the objectives is also considered in detail. We further develop a formula for the lower bound of energy cost to help residents or engineers quickly choose best parameters of RES and ESS for their homes during the installation process. The performance of our system is verified through extensive simulations under three different scenarios of normal, economic, and smart with different selling prices using real data, and simulation results are compared in terms of daily energy cost, PAR, user's convenience and consecutive waiting time to use appliances. Numerical results clearly show that the economic scenario achieves 51.6% reduction of daily energy cost compared to the normal scenario while sacrificing the user's convenience, PAR, and consecutive waiting time by 49%, 132%, and 1 hour, respectively. On the other hand, the smart scenario shows only slight degradation of user's convenience and PAR by 2% and 18%, respectively while achieving 46.4% reduction of daily energy cost and the same level of consecutive waiting time. Furthermore, our simulation results show that a decrease of selling prices has tiny impacts on PAR and user comfort even though the daily energy cost increases.INDEX TERMS Home energy management systems, electricity selling operation, energy trading, MINLP, user comfort, lower bound.

Applying masking tape to a particular area is a very important step for protecting an uninvolved surface in processes like mechanical part repairing or surface protection. In the past, the task was very time-consuming and required a lot of manual works. In recent years, with some advances in the fields of automatic robotic system and computer vision, the task now can be completed with the help of an automatic taping system containing a 3D scanner, a manipulator and a rotating platform. This implementation has been proved to provide better quality and be at least twice as fast as comparing to the work done by a human operator. However, there are still some limitations of this setup. First, it is difficult for the user to monitor the taping process since the system uses the 3D scanner to reconstruct the surface model and there is no calibrated projector to overlay the manipulator's trajectory over the real surface. Second, the main user is supposed to use a computer with keyboard and mouse to identify the area for masking which requires some expert knowledge and might not be appropriate in an industrial context where people wear protective equipment such as gloves or helmet. This paper introduces the use of spatial augmented reality technology and wearable device in the semi-automatic taping robotic system and the related calibration algorithms to enhance the user experience. The framework and its components are presented, with a case study and some results.

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Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate (MMR) pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different CEMSs for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the community with the proposed CEMS has the lowest daily energy cost among three CEMSs. In particular, the community with the proposed CEMS only has $$78\%$$ 78 % of the daily energy cost of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community. We finally investigate the trade-off of the MMR pricing in the local energy trading of the community, which allows the profits of different types of homes to be flexibly adjusted.