Smart Grid, Smart Controllers and Home Energy Automation—Creating the Infrastructure for Future

Integration of unpredictable renewable power sources into the Grid is leading to the development of wide area control algorithms and smart grid. Smart meters are the first step in the building a smart consumer interface. Much more, however, would be required in building a smart grid than just smart meters. This paper explores the conceptual architecture of smart grid. It highlights the need for additional infrastructure to realize full potential of smart grid. The information presented in this paper is an attempt to uncover what the future in smart grid could be and what infrastructure would be required to tap its potential. As smart grid evolves, more functionality would be built in the constituents. The paper also proposes mathematical basis for some of the controller algorithms.

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

A Smart Home Energy Monitoring System Based on Internet of Things and Inter Planetary File System for Secure Data Sharing

Energy demand will continue to rise as a result of predicted population growth. In this work, a user-friendly home energy monitoring system based on IoT is described, which is capable of collecting, analyzing, and displaying data. Users register their sensors and devices on the monitoring platform. PostgreSQL and Elasticsearch databases are used to store the resulting measurements. In a smart home, the wireless sensor ACS712 was used to monitor the flow of electricity (current and voltage) for a household device. The user can share data about electricity consumption and costs with a third party via the private IPFS (InterPlanetary File System) network. A third party can download all the energy consumption data for a device or many devices from the platform for 1 day, 3 months, 6 months, and 1 year. The studies on the development of energy-efficient technology for home devices benefit greatly from the gathered data. For security in the system, it is preferred to run Keyrock Idm, Wilma Pep Proxy, and Orion Context Broker in HTTPS mode, and MQTTS is used to retrieve sensor data. The experimental results showed that the energy monitoring system accurately records voltage, current, active power, and the total amount of power used and offers low-cost solutions to the users using household devices in a day.

Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange

This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.

Renewable and Storage Energy Integration for Smart Grid Housing

This paper presents the design, implementation and testing of an embedded system that integrates solar and storage energy resources to smart homes within the smart mierogrid. The proposed system provides the required home energy by installing renewable energy and storage devices. It also manages and schedules the power flow during peak and off-peak periods. In addition, a two-way communication protocol is developed to enable the home owners and the utility service provider to improve the energy flow and the consumption efficiency. The system can be an integral part for homes in a smart grid or smart microgrid power networks. A prototype for the proposed system was designed, implemented and tested by using a controlled load bank to simulate a scaled random real house consumption behavior. Three different scenarios were tested and the results and findings are reported. Moreover, data flow security among the home, home owners and utility server is developed to minimize cyber-attaeks.

Commercial Technologies for Advanced Light Control in Smart Building Energy Management Systems: A Comparative Study

This work investigates the economic, social, and environmental impact of adopting different smart lighting architectures for home automation in two geographical and regulatory regions: Algiers, Algeria, and Stuttgart, Germany. Lighting consumes a considerable amount of energy, and devices for smart lighting solutions are among the most purchased smart home devices. As commercialized solutions come with variant features, we empirically evaluate through this study the impact of each one of the energy-related features and provide insights on those that have higher energy saving contribution. The study started by investigating the state-of-the-art of commercialized ICT-based light control solutions, which allowed the extraction of the energy-related features. Based on the outcomes of this study, we generated simulation scenarios and selected evaluations metrics to evaluate the impact of dimming, daylight harvesting, scheduling, and motion detection. The simulation study has been conducted using EnergyPlussimulation tool, which?enables fine-grained realistic evaluation. The results show that adopting smart lighting technologies have a payback period of few years and that the use of these technologies has positive economic and societal impacts, as well as on the environment by considerably reducing gas emissions. However, this positive contribution is highly sensitive to the geographical location, energy prices, and the occupancy profile.

Power Scheduling with Max User Comfort in Smart Home:Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Nowadays,smart electricity grids are managed through advanced tools and techniques.The advent of Artificial Intelligence(AI)and network technology helps to control the energy demand.These advanced technologies can resolve common issues such as blackouts,optimal energy generation costs,and peakhours congestion.In this paper,the residential energy demand has been investigated and optimized to enhance the Quality of Service(QoS)to consumers.The energy consumption is distributed throughout the day to fulfill the demand in peak hours.Therefore,an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption.This model gives priority to consumer preferences while planning the operation of appliances.A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes.Furthermore,the allotment mechanism has been designed to manage the grid appliances through the edge node.The proposed model helps to improve the latency in the grid appliances scheduling process.

Towards Optimal Planning and Scheduling in Smart Homes

This research addresses the planning and scheduling problem in and among the smart homes in a community microgrid. We develop a bi-linear algorithm, named ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy trading among the smart homes in a community microgrid. However, P2P trading potentially results in an unfair cost distribution among the participating households. To the best of our knowledge, the ECO-Trade algorithm is the first near-optimal cost optimization algorithm which considers the unfair cost distribution problem for a Demand Side Management (DSM) system coordinated with P2P energy trading. It also solves the time complexity problem of our previously proposed optimal model. Our results show that the solution time of the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Furthermore, we analyze the solutions and identify that the algorithm sometimes gets trapped at a local minimum because it alternately sets the microgrid price and quantity as constants. Finally, we describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization.

Active and passive modulation of solar light transmittance in a uniquely multifunctional dual-band single molecule for smart window applications

Functional materials may change color by heat and electricity separately or simultaneously in smart windows.These materials have not only demonstrated remarkable potential in the modulation of solar radiation but are also leading to the development of indoor environments that are more comfortable and conducive to improving individuals'quality of life.Unfortunately,dual-responsive materials have not received ample research attention due to economic and technological challenges.As a consequence,the broader utilization of smart windows faces hindrances.To address this new generational multistimulus responsive chromic materials,our group has adopted a developmental strategy to create a poly(NIPAM)n-HV as a switchable material by anchoring active viologen(HV)onto a phase-changing poly(NIPAM)n-based smart material for better utility and activity.These constructed smart windows facilitate individualistic reversible switching,from a highly transparent state to an opaque state(thermochromic)and a red state(electrochromic),as well as facilitate a simultaneous dual-stimuli response reversible switching from a clear transparent state to a fully opaque(thermochromic)and orange(electrochromic)states.Absolute privacy can be attained in smart windows designed for exclusive settings by achieving zero transmittance.Each unique chromic mode operates independently and modulates visible and near-infrared(NIR)light in a distinct manner.Hence,these smart windows with thermal and electric dual-stimuli responsiveness demonstrate remarkable heat regulation capabilities,rendering them highly attractive for applications in building facades,energy harvesting,privacy protection,and color display.

Optimal gateway deployment under different queuing mechanisms in smart grid

By optimizing the network topology, this paper proposes a newmethod of queuing theory clustering algorithm based on dynamic programming in a home energy management system（ HEMS）. First, the total cost of the HEMS system is divided into two parts, the gateway installation cost and the data transmission cost. Secondly, through comparing two kinds of different queuing theories, the cost problem of the HEMS is converted into the problem of gateway deployment. Finally, a machine-to-machine（ M2M） gateway configuration scheme is designed to minimize the cost of the system. Simulation results showthat the cost of the HEMS system mainly comes from the installation cost of the gateways when the gateway buffer space is large enough. If the gateway buffer space is limited, the proposed queue algorithm can effectively achieve optimal gateway setting while maintaining the minimal cost of the HEMS at desired levels through marginal analyses and the properties of cost minimization.

Improvements in energy saving and thermal comfort for electric vehicles in summer through coupled electrochromic and radiative cooling smart windows

In hot climates,the large amount of cooling load in electric vehicle(EV)results in a lot of battery energy consumption,leading the decrease of driving range.With the widespread application of windows in EV,the electrochromic glass(EC)shows great prospect in lowering the cooling load.However,researches on the application of EC in EV lack the consideration of both passive cooling measures and passenger comfort,which limits the further application of EC.In this paper,we proposed an idea combining the novel techniques of both electrochromism and radiative cooling.Computational fluid dynamics(CFD)is modeled to simulate the application of electrochromic and radiative cooling coupled smart windows in hot parking conditions,exploring the improvement effect of the window on the thermal environment,comfort and energy saving of the EV.The results indicate that,under the intense sunlight with an outdoor temperature of 33℃,activating the air conditioning to maintain an average interior temperature of 26℃,the coupled windows reduced the cooling capacity of the air conditioning by 762 W compared to regular windows,which can further increase the range of EV.Meanwhile,compared to simple electrochromic fully colored glass,the integration of radiative cooling technology can lower the window surface temperature by up to 10.7℃.Moreover,compared to regular windows,the coupled windows lowered the standard effective temperature(SET*)for passengers by approximately 7℃,significantly improving comfort.These research findings are expected to provide guidance for optimizing window design and enhancing the performance of EV.

A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences

Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs;a model of household loads is proposed;constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving;the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.

Low Carbon Building Design Optimization Based on Intelligent Energy Management System

The construction of relevant standards for building carbon emission assessment in China has just started,and the quantitative analysis method and evaluation system are still imperfect,which hinders the development of low-carbon building design.Therefore,the use of intelligent energy management system is very necessary.The purpose of this paper is to explore the design optimization of low-carbon buildings based on intelligent energy management systems.Based on the proposed quantitative method of building carbon emission,this paper establishes the quota theoretical system of building carbon emission analysis,and develops the quota based carbon emission calculation software.Smart energy management system is a low-carbon energy-saving system based on the reference of large-scale building energy-saving system and combined with energy consumption.It provides a fast and effective calculation tool for the quantitative evaluation of carbon emission of construction projects,so as to realize the carbon emission control and optimization in the early stage of architectural design and construction.On this basis,the evaluation,analysis and calculation method of building structure based on carbon reduction target is proposed,combined with the carbon emission quota management standard proposed in this paper.Taking small high-rise residential buildings as an example,this paper compares and analyzes different building structural systems from the perspectives of structural performance,economy and carbon emission level.It provides a reference for the design and evaluation of low-carbon building structures.The smart energy management system collects user energy use parameters.It uses time period and time sequence to obtain a large amount of data for analysis and integration,which provides users with intuitive energy consumption data.Compared with the traditional architectural design method,the industrialized construction method can save 589.22 megajoules(MJ)per square meter.Based on 29270 megajoules(MJ)per ton of standard coal,the construction area of the case is about 8000 m2,and the energy saving of residential buildings is 161.04 tons of standard coal.This research is of great significance in reducing the carbon emission intensity of buildings.

Automated Smart Utilization of Background Lights and Daylight for Green Building Efficient and Economic Indoor Lighting Intensity Control

The ways which are used today in order to light houses, offices, and most of the indoor areas are inefficient as a lot of energy is consumed unnecessarily during the day time. Mainly this problem because the interior lighting design consider the worst case when the light service is at night, which is not always valid. Also in most cases the lighting system design relies on people to control the lights switching on and off. This problem is also one of the design concerns in Green Building. In this paper, a solution to this problem and a method for people’s comfort who use the indoor facilities in industrial buildings is presented. In the proposed smart lighting system, lights switch on automatically when there is somebody in the room or in the occupied space and switch off when there is no occupancy. In addition to this known technique, adjustment of the brightness level of the lights will be possible via the personal computer or any other smart device. In this method, for the illumination level in the area, where is needed to be controlled for better energy saving, the light automatically is measured by the sensor and considering the amount of background lights coming from outside, automatically the brightness of lights is controlled to reach the preset level that determined for that room. By the means of this method, it is possible to provide better user comfort, avoid human forcedness to switch the light on and off, and hence effective energy saving. Arduino controller is used to build the controller and to demonstrate the results. Economic analysis was done to calculate the percentage of the energy saving that can be obtained by implementing the proposed smart lighting controller. As an outcome of the economic analysis, energy saving norm for an office with a standard size was calculated.

Supply and Demand Oriented Energy Management in the Internet of Things

The Internet of Things (IoT) is emerging as an attractive paradigm involving physical perceptions, cyber interactions, social correlations and even cognitive thinking through a cyber-physical-social-thinking hyperspace. In this context, energy management with the purposes of energy saving and high efficiency is a challenging issue. In this work, a taxonomy model is established in reference to the IoT layers (i.e., sensor-actuator layer, network layer, and application layer), and IoT energy management is addressed from the perspectives of supply and demand to achieve green perception, communication, and computing. A smart home scenario is presented as a case study involving the main enabling technologies with supply-side, demand-side, and supply-demand balance considerations, and open issues in the field of IoT energy management are also discussed.

New Design and Architecture of a Smart Tracker： Flexible and Scalable for PV and CSP Systems

This work proposes a new design and architecture of a flexible biaxial solar tracker. A new approach was adopted with the use of a two separated cards, the smart and power card in a scalable concept. This module allows a more saving of energy in comparison with the fixed systems for PV （photovoltaic） application and allows hire performances for CSP （concentrated solar power） systems. It provides a significant added value for higher power applications in comparison with the existing system. The developed sun tracking system is autonomous, flexible, scalable and low cost system.

Power ＂Smart Pricing＂ with and without Smart Meters

Over the past several years, the Taiwan Power Company has launched two smart pricing programs to assess the demand response of residential customers： the TOU （time-of-use） rate scheme and the DRI （demand reduction incentive） scheme. This paper discusses these two programs and evaluates their respective performances. We develop an efficient approach based on marginal cost pricing to redesign the TOU rate scheme. In our finding, the TOU price levels could be revised to encourage more customers to participate by enlarging the price gap. Moreover, the DRI scheme can be further improved in order to reach an efficient win-win solution among customers, the utility and society. This can be achieved via a careful design of incentive tariff discounts to take account of the time-of-use or location-specific features of the power supply/demand condition.

秦岭天台山超长公路隧道群关键技术创新与实践

针对秦岭天台山超长公路隧道群在建设和运营过程中面临的工程建设难度大,运营管理复杂、安全风险高以及运营能耗高等诸多技术难题,围绕安全智慧运营、安全快速施工、单层衬砌支护、自然能源利用等方向,系统总结了秦岭山区特殊地质地形条件下32 km隧道群建设与运营过程中系列创新技术成果。主要结论:1)通过超长隧道群安全智慧运营技术创新与应用,破解山区超长连续公路隧道群运营安全保障难题;2)通过公路隧道安全快速施工技术创新与应用,突破超长大断面公路隧道机械化安全快速施工瓶颈;3)通过公路隧道单层衬砌支护技术创新与应用,构建硬质岩公路隧道单层衬砌支护体系;4)通过公路隧道利用自然能源技术推广应用,探索超长公路隧道绿色低碳运行。

农作物秸秆低碳墙体材料研究现状

综述了近年来国内外关于农作物秸秆在建筑墙体材料中的应用及研究进展,从生产技术、制备工艺、配合比设计等方面出发,重点介绍了农作物秸秆低碳墙体材料的力学性能、保温隔热性能、阻燃性能等优良特性,并对农作物秸秆低碳墙体材料现阶段所存在的缺陷和不足给出了合理建议,对其未来发展方向及应用前景做出了展望。