Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system

Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels

In this paper, we consider a station-based electric car-sharing system which allows one-way trips, and uses relocation to re-balance the vehicle distribution. We adopt the point-of-view of a service provider, whose objective is to maximize the profit associated with the trips performed by users. We introduce an exact relocation model for operating hours, and we explicitly consider the consumption and recharge process of electric vehicles batteries. In addition, the model is extended to the relocation operations to be performed at night, namely when the system is not operating. We also describe two model-based heuristics developed to solve the relocation model for operating hours on large-scale systems. The paper is concluded by a set of computational experiments on realistic data derived from an existing car-sharing system. The experiments investigate the scalability of the proposed model and highlight the circumstances under which the relocation operations can improve the system performance.

Optimizing relocation operations in electric car-sharing

A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid

Clean power production, buildings, and transportation are key areas for climate change mitigation. Their tighter integration decreases not only the emissions, but also the energy consumption of buildings and transportation. Energy integration and interactions between buildings and vehicles are dependent on the type of building, vehicle, and renewable energy system, as well as the local climatic conditions. The current academic literature does not provide a systematic analysis of this topic. In the study, different energy management systems and advanced energy control strategies have been formulated to study such interactions both from a building and a vehicle perspective. Furthermore, technical solutions have been systematically reviewed in terms of the enhancement of energy interaction capabilities, in particular from the standpoint of renewable energy systems, energy/fuel charging facilities, and control strategies. Assessment criteria employed in the review of solutions include grid interaction, annual operational cost, annual net CO2 emissions, and annual matching capability. The literature review identifies several technical challenges that need further consideration such as capacity expansion and power fluctuation of the electric grid, low efficiency of heat recovered from electricity generation, and depreciation of vehicles. The future outlook and potential for the energy interaction networks between buildings and vehicles have also been presented.

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Energy integration and interaction between buildings and vehicles: a state-of-the-art review

Electric vehicles (EVs) are becoming an attractive alternative to gasoline vehicles owing to the increase of greenhouse gas emissions and gasoline prices. EVs are also expected to function as battery storages for stabilizing large fluctuations in the power grid through the vehicle-to-grid power system, which requires smart charge and discharge scheduling algorithms. In this paper, we develop a linear programming based heuristic algorithm on a time–space network model for charge and discharge scheduling of EVs. We also develop an improved two-stage heuristic algorithm to cope with uncertain demands and departure times of EVs, and evaluate the effect of the smart charge and discharge scheduling of EVs on a peak load reduction in a building energy management system.

A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

The conventional wireless sensor networks are designed to gather information from sensors, and pervasive computing systems involve such wireless sensor networks that consist of a number of tiny-/micro-sensors to sense the real-world objects and phenomena. In addition, they also need to conduct training tasks for highly-intelligent processing like pattern recognition and anomaly detection, but doing such tasks in cloud servers may often be impossible or expensive due to lack of communication facility, data transfer overhead, privacy concerns and so on. In this paper, we propose a unique approach to carry out deep learning (both training/testing) among wireless sensors nodes for in-situ, edge-heavy processing. The idea is to appropriately assign neurons of CNN (Convolutional Neural Network) to wireless nodes, each of which has limited processing capability but can have some power when they are united. For this purpose, we have designed a distributed, restricted version of CNNs. We conducted two experiments using real data; one is for anomaly detection of temperature in an over-1,400m^2 lounge space using 50 temperature sensors to confirm the learning capability as well as communication overhead, and another is for activity recognition using a 6x6 array of thin-, energy-efficient film-type infra-red sensors with micro-processors to demonstrate our concept.

MicroDeep: In-network Deep Learning by Micro-Sensor Coordination for Pervasive Computing

Radiative defects in thermally grown SiO2/4H-SiC(0001) structures and their location in depth were investigated by means of cathodoluminescence spectroscopy. It was found that while luminescence peaks ascribed to oxygen vacancy and nonbridging oxygen hole centers were observed both from thermal oxides grown on (0001) Si-face and C-face surfaces as with thermal oxides on Si, intense yellow luminescence at a wavelength of around 600 nm was identified only from the oxide interface on the Si-face substrate regardless of the oxide thickness and dopant type. Possible physical origins of the radiative centers localized near an oxide interface of a few nm thick are discussed on the basis of visible light emission from Si backbone structures.

Cathodoluminescence study of radiative interface defects in thermally grown SiO2/4H-SiC(0001) structures

Al-doped CsLiB 6 O 10 crystals were grown by the top-seeded solution growth method in large sizes with good optical quality. We reveal for the first time that these crystals exhibit outstanding UV-induced damage resistant performance.

Al-doping in CsLiB 6 O 10 for high resistance against UV laser-induced damage

We present a nanoscale ruler, which enables us to measure a length in a molecule based on fluorescence resonance energy transfer. Experimental results show that the fluorescence signal changes depending on the measured length.

Yuta Fukushima

Papers

A linear programming based heuristic algorithm for charge and discharge scheduling of electric vehicles in a building energy management system

MicroDeep: In-network Deep Learning by Micro-Sensor Coordination for Pervasive Computing

Cathodoluminescence study of radiative interface defects in thermally grown SiO2/4H-SiC(0001) structures

Al-doping in CsLiB 6 O 10 for high resistance against UV laser-induced damage

A nanoscale ruler by use of fluorescence resonance energy transfer and DNA nano-structure