• Energy Research

Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper demand side management schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and proposes a personalized electricity retail plan recommender system for residential users. The proposed personalized recommender system (PRS) is based on the collaborative filtering technique. The energy consumption data of users are firstly collected from the smart meter, and then key energy consumption features of the users are extracted and stored into a user knowledge database (UKD), together with the information of their chosen electricity retail plans. For a target user, the recommender system analyzes his/her energy consumption pattern, find users having similar energy consumption patterns with him/her from the UKD, and then recommend most suitable pricing plan to the target user. Experiments are conducted based on actual smart meter data and retail plan data to verify the effectiveness of the proposed PRS.

The ever-increasing power demand and more frequent extreme weather results in increasing number of grid outage events. This paper proposes a resilient energy management scheme for a building community prototype installed with community-scale photovoltaic solar panel and Battery Energy Storage System (BESS), aiming to enhance self-power supply capability of the community during the grid outage period. The scheme harnesses the community-scale renewable energy source and Battery Energy Storage System (BESS), and flexibility of building load shifting to maximize a warfare objective, i.e. the sum of served load of all buildings in the community over the grid outage horizon. An optimization algorithm previously proposed by the authors – Natural Aggregation Algorithm (NAA) is applied to solve the energy management model. Simulations are conducted based on the Australian "Smart Grid, Smart City" dataset to validate the effectiveness of the proposed method.

Abstract In the context of global warming, radiative coolers with high solar reflectance and strong emissivity in the atmospheric window can cool the substrate as well as the ambient air. Silica at its nano or micro-scale being randomly dispersed into a uniform transparent polymer can form scalable radiative coolers for large-scale application. Promising cooling performance has been reported for silica-polymers compared with conventional cooling materials, but their performance can be largely influenced by various fabrication parameters. So far, how fabrication parameters influence the emissivity and the cooling performance has not been experimentally demonstrated and the cooling capacity of silica-polymers reported was not substantial compared to other superior radiative coolers. In this work, random silica-polymer has been optimized experimentally. Lab measurement and experimental testing of six fabricated silica-polymers under subtropical and desert climates indicated that due to the complexity of the thermo-radiative balance, high emissivity and strong selectivity are both indispensable in the production of high cooling power. If combined with superior reflectors with higher solar reflectance and especially the emissivity in 8–13 μm enhancing the heat dissipation ability, substantial cooling capacity can be achieved: under the harsh desert climate with average peak solar radiation over 1100 Wm-2, the combination presented sub-ambient temperature of maximum 4.7 °C when air temperature reached its peak and the maximum daytime and night-time sub-ambient temperatures were 12.5 °C and 15.9 °C respectively.

Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as “highly responsive users” and “less responsive users” based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator “SimHouse” are designed to validate the proposed system.

Abstract Passive daytime radiative cooling represents one of the boldest answers to tackle the future cooling needs of the built environment and to mitigate urban heat island effects. Recent developments in the field targeted sub-ambience with several successful examples. On the other side, heating demands may get exacerbated unless effective countermeasures against overcooling are identified, especially in wintertime or heating-dominated climates. This review aims at collecting state-of-the-art technologies and techniques to dynamically control the heat transfer to and from the radiative emitter and ultimately modulate its cooling capacity. Potential solutions are selected from different applicative fields, including spacecraft thermal control, thermal camouflage and electronics. Environmentally-responsive solutions are analyzed in depth given their perfect match with radiative cooling design requirements. Among them, VO2-tuned Fabry-Perot resonators are given particular emphasis, owing to their proven applicability. Active solutions are presented for completeness, but in less detail. Underlying principles, structural composition and experimental/simulated results are detailed and discussed to identify prominent pathways towards technically and economically effective integration in the built environment.

Abstract Daytime radiative cooling is regarded as the gold promise of future sustainable building energy systems and a breakthrough in the fight against local climate change. Despite the fervid research interest, most literature reports exceptional theoretical performances under ideal, desert-like conditions, but overlooks the cooling impairment that occurs under low atmospheric transparency (cloudy, humid, polluted conditions) and reduced sky access (packed urban contexts). Power recovery and stabilization call for decoupling of incoming and outgoing radiation at equal wavelengths. Enhanced directionality and high-contrast, broadband asymmetric transmission have been recently proposed to expand the applicability of radiative coolers over a wider spectrum of climates, weathers and terrains. This review offers itself as a first, timely synthesis of the current technological arena. Physical principles, materials and designs, collected from a variety of applicative fields, are detailed and discussed in terms of performance and feasibility, to inspire the transition into sustainable building cooling, worldwide. Major grey areas and serious concerns on potential violations of the 2nd law of thermodynamics reinforce the need for experimental demonstrations in future research.