Advanced energy consumption system for smart farm based on reactive energy utilization technologies

Over the decades, both agriculture and energy grids have encountered significant challenges, such as the lack of power supply to agricultural farms and the difficulties of renewable energy use in the electricity sector. To overcome and address these issues, this paper presents an idea of merging smart farm and renewable energy, using surplus renewable energy for the agriculture sector using the smart farm for renewable energy supplies. First, demonstrate the framework to connect a smart farm energy system and analyze its feasibility and advantages. In this paper, Reactive Energy Utilization Technology (REUT) has been used as an efficient energy consumption system for the smart farm. It is used to predict the optimum wind power scenario in the smart farm by monitoring continuous control variables, with the lowest voltage reached in each scenario, and minimized actual total power losses. A combined system consisting of smart farming production, as well as renewable energy consumption, has been evaluated in experimental results. Experimental results and discussion shows that the proposed method has better renewable energy consumption, accuracy(96.7%), and efficiency(95.6%) for smart farming.     

The technological innovation of hybrid and plug-in electric vehicles for environment carbon pollution control

Plug-in Hybrid Electric Vehicles (PHEV) have been promoted by providing Vehicle-to-Grid (V2G) infrastructure as a possible solution to reduce greenhouse gas (GHG) and other emissions by utilizing energy instead of oil for effective environmental management. The promising solution for reducing air pollution in cities is commonly regarded as electric vehicles, which helps to optimize the environment management more effectively, as a key to future low carbon mobility. However, their environmental benefits rely on the temporal and spatial sense of real use, and challenges such as limited range complicated for the rollout of an Electric Vehicle (EVs). This paper investigates the environmental carbon pollution in cities and control preventions using Plug-in Hybrid Electric Vehicles (PHEV). Further, the Artificial intelligence model has been introduced, which defines optimal automobile designs and the assignment of vehicles to drivers across a variety of scenarios, including minimum net life cycle expense, GHG emissions, and oil usage for effective environmental management. By designing overspent vehicle power for corresponding output, weight, and cost impact, the life cycle costs and the emission of GHG are reduced utilizing high battery swinging and replacing batteries as needed. Moreover, energy consumption (EC) and pollution have been greatly influenced by the use of energy sources in the environment. The significant energy consumption and pollution variables resulted in a large proportion of coal-fired energy. The results show that the PHEV can achieve better fuel economy by combining the proposed model with an allowable deviation from the state of the charge.     

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10¹⁰⁰ feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets.     

The need for local adaptation of smart infrastructure for sustainable economic management

At present, there are several growing problems for sustainable urban growth, and the typical policy strategies to tackle them are inadequate. In recent years, several governments have initiated numerous smart city and smart infrastructure programs aimed at enhancing the quality life of the people and helping town managers improve the public infrastructure activity and management. The usage of the Internet of Things (IoT) for infrastructure system enables the gathering, storing, incorporation, and analysis of large quantities and varieties of information relating to the status and output of infrastructure systems together with public activity through Cloud-based asset management systems, mobile apps, and Big Data analytics. In this paper, a master data management (MDM) approach has been proposed to unlock the importance of comprehensive network data for efficient, safe, and robust community development. In the market field, MDM is implemented for the organization of big data organizational and research applications. The proposed method includes an effective MDM solution for safe, efficient, and functional community design, commercial and open source MDM platforms, city development principles, smart city concept models, adaptive shared network backgrounds, semantic cloud technology, and specialists responsible for designing development, scalable platform technology for evaluating the input.     

A dynamic analysis of the global warming potential associated with air conditioning at a city scale: an empirical study in Shenzhen,China

Heating, ventilation and air conditioning (HVAC) systems are a major source of energy consumption in buildings, directly and indirectly contributing to greenhouse gas (GHG) emissions. In the urban environment, and depending on local climatic conditions, air conditioning units attribute to these high energy demands. This study analyzes the use of residential air conditioning units and their associated global warming potential (GWP) between 2005 and 2030 for the city of Shenzhen, a fast-growing megacity located in Southern China. A life cycle assessment approach was adopted to quantify the GWP impacts which arise from both direct (refrigerant release) and indirect (energy consumption) sources, in combination with a materials flow analysis approach. The results show that the total GWP (expressed as carbon dioxide equivalents, CO₂ eq.) from residential air conditioning systems increased from 2.2 ± 0.2 to 5.1 ± 0.4 million tonnes (Mt) CO₂ eq. between 2005 and 2017, with energy consumption and refrigerant release contributing to 72.5% and 27.5% of the total demands, respectively. Immediate measures are required to restrict refrigerant release and reduce the energy consumption of air conditioning units, to help mitigate the predicted additional total emissions of 36.4 Mt. CO₂ eq. potentially released between 2018 and 2030. This amount equals to approximately New Zealand's national CO₂ emissions in 2017. The findings proposed in this study targets air conditioning units to reduce the GWP emissions in cities, and provide useful data references and insights for local authorities to incentivise measures for improving building energy efficiency management and performance.     

Benchmarking environmental and economic impacts from the HSR networks considering life cycle perspectives

This paper unprecedentedly benchmarks the environmental and economic impacts of notable High-speed rail (HSR) networks. The goals are to (i) point out the environmental impacts from the HSR networks and (ii) evaluate the whole life cycle cost of HSR systems. The emphasis of this study is placed on five HSR networks from five countries to depict the effectiveness of sustainable transport policies in each particular country. Both life cycle assessment (LCA) and life cycle cost (LCC) models are adopted for a new critical framework capable of benchmarking the lifecycle sustainability of HSR networks. The new findings exhibit that CRC's system is the leader in energy-saving, who consumes only 67.55 GJ/km yearly, and emits lowest CO₂ at an amount of 77,532.32 tCO₂/km annually. These impressive results are stemmed from key enabling policies related to eco-friendly rolling stock design, sustainable construction, and green energy grids. With respect to the LCC analysis, the SCNF network takes advantage in the economy of scale and unleashes the lowest cost among other networks. It estimates that the SNCF network spends approximately 1,990,599.51 £/km annually at a % discount rate. The implications of these finding are discussed that the initial project has a high chance to be successful on economic than the late project due to an influence of the time value of money.     

Enhancing public building energy efficiency using the response surface method: An optimal design approach

Today, energy problems are becoming increasingly serious. The direct energy consumption of buildings accounts for 20% of the total energy consumption in a country. There are difficulties in continuing the mode of high energy consumption in the traditional construction industry. Therefore, the future of construction is in the development of green buildings. In the life cycle of a building, the consumption during the construction phase accounts for only approximately 20% of the total energy consumption. Most of the consumption occurs during buildings operations, such as lighting, heating, air conditioning and the running of various electrical appliances. Therefore, this paper focuses on the energy consumed during the building operation period with the aim of optimizing relevant design parameters to reduce total energy consumption. The West Twelfth Teaching Building (WTTB) of Huazhong University of Science & Technology (HUST) is used as a prototype, and Design Builder is used to establish a model of energy consumption and validate the reliability of the model based on the data obtained from the investigation. Based on this model, the study takes the perspective of energy conservation to analyze ten factors that may affect the energy consumption of the building: the heat transfer coefficient of the roof, the amount of fresh air, the heat transfer coefficient of interior walls, the heat transfer coefficient of the floors, the interior temperature, the energy efficiency ratio of the air conditioner, the thickness of the outer wall insulation, the ratio of windows to walls, the natural ventilation starting temperature and the solar heat gain coefficient of the outer windows. Those factors are then ranked according to their energy-saving potential through partial factorial design tests. The six factors with the most potential for energy savings are selected and divided into 2 groups to conduct a response surface optimization analysis of three factors at three levels. The best level of each factor and the optimal combination of all factors are obtained to reduce building energy consumption to the greatest possible extent and to provide a reference for teaching buildings and similar public buildings (PBs) in achieving the goal of “green building.”     

Energy storage selection for sustainable energy development: The multi-criteria utility analysis based on the ideal solutions and integer geometric programming for coordination degree

The choice of the energy storage technology involves multiple criteria that need to be simultaneously considered in the energy planning process. The development of sustainable energy system requires to take into account not only technical characteristics of storage technologies but also to pursue sustainability issues. The paper aims to perform not-site-specific sustainability assessment of the main types of energy storage technologies (mechanical, chemical, electrochemical and thermal) based on the developed advanced multi-criteria technique. The paper follows the vein of the coordinated Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and extends it to the interval environment. A geometric integer programming model is introduced to identify the interval for the degree of coordination. The proposed method is, therefore, able to handle uncertainty and coordinate performance of the alternatives with respect to multiple criteria. Sustainability assessment of energy storage technologies is performed based on the most important technological, economic, environmental and social criteria. The results showed that the most sustainable energy storage technologies are mechanical and thermal ones (utility scores range in between 1 and 0.75). The remaining technologies exhibit utility scores of 0.69 at most. The least appealing options are electrochemical storage technologies.     

Life cycle impact assessment of home energy management systems (HEMS) using dynamic emissions factors for electricity in Finland

Decarbonising the European economy is a long-term goal in which the residential sector will play a significant role. Smart buildings for energy management are one means of decarbonisation, by reducing energy consumption and related emissions. This study investigated the environmental impacts of smart house automation using life cycle impact assessment. The ReCiPe method was selected for use, in combination with dynamic emissions factors for electricity in Finland. The results indicated that a high level of technology deployment may be counter-effective, due to high electricity consumption by the sensor network, automation system and computing devices. The results also indicated that number of inhabitants per household directly affected the environmental impacts of home automation. A single-person household saw its environmental impacts increase by 15%, while those of a five-person household increased by 3% in the worst-case scenario. The manufacturing phase contributed the major share of environmental impacts, exceeding the use phase in multiple categories. These findings indicate that finding the sweet spot in which technology can promote decarbonisation will be crucial to achieving the goal of a low‑carbon economy.     

The contribution of sensor-based equipment to life cycle assessment through improvement of data collection in the industry

Within the paradigm of “Industry 4.0”, data collection and management acquire new relevance. In this context, the introduction of the Internet of Things (IoT) in the manufacturing field allows to develop information systems able to offer the possibility of using a large amount of data collected from heterogeneous sources in real time. The information thus obtained is functional not only to the attainment of an industrial development but also for evaluation and improvement of its sustainability. For example, the benefit obtainable for the life cycle assessment (LCA) methodology from the new digital configuration is represented by a greater specificity of data that wireless sensors networks (WSN) measure and transmit in a continuous way.This paper explores the benefit of using a sensor for the evaluation of the environmental impact, in particular for LCA, in an Italian company producing chemical formulations. The machinery electric consumption directly measured data is compared with the same data estimated with mathematical models. It highlights how much different approach of data measurement and calculation can affect the LCA results. The detection of the energy variable related to a single machine for a particular process, which has been described in the paper, represents a first step towards the implementation of a WSN, proposed by Scatol8 srl, to be used to produce in a more sustainable way.     

CONCRETop - A multi-criteria decision method for concrete optimization

CONCRETop is a novel multi-criteria decision method developed by the authors of this study for concrete optimization, which can be used to optimize either conventional or non-conventional concrete mixes. It can be used to identify the main differences between concrete mixes in terms of three perspectives, namely quality performance, environmental impacts and cost efficiency, and the corresponding causes.The characteristics of concrete fundamentally depend on its target application (e.g. the structural design project). In other words, it depends on the requested characteristics demanded by the consumer. However, this method allows optimizing concrete mixes based on the three mentioned perspectives even if the specific characteristics of concrete for the selected application are unknown. For complicated cases like this one, five scenarios (business as usual, green, strength, service life, and cost) were proposed with different threshold values, in order to cover most of the construction cases (the majority of concrete applications).The major outcome of this method is to provide researchers with a validated approach to support the development of non-conventional materials with optimized technical performance and minimum cost, environmental impact and natural resources' consumption. The method is valid for any case studies and concrete mixes worldwide. Most importantly, this optimization process may change how we take care of the next generation and find sustainable alternative materials for the concrete industry.     

Holistic cognitive conflict chain management framework in supply chain management

Supply chain management is an integral part of most businesses and crucial to company performance and customer satisfaction. The demanding characteristics in supply chain management include lack of customer service quality, increased cost, risk management, and lack of inefficiency, which are considered indispensable factors in this research. This paper proposes a holistic cognitive conflict chain management framework (HCCCMF) to enhance quality customer service and supply chain management efficiency. This proposed HCCCMF method reduces interruption during the process and the cost of raw materials, labour, and energy in supply chain management. Behavioural monitoring analysis is carried out to improve quality service to consumers, creating an excellent customer-supplier relationship to function effectively in supply chain management. Policy matrix analysis is introduced to overcome disruptions during operation, which effectively manages affordable rates. The experimental results show that the proposed HCCCMF method enhances the productivity ratio of 94.3%, performance ratio of 98.4%, efficiency ratio of 96.5%, reliability ratio of 95.5%, accuracy ratio of 97.8%, and low trade cost ratio of 15.3% when compared to other existing methods.     

基于精细化管理的环境物联网发展趋势

环境物联网运用智能传感器等设备感知生态环境的变化,监测环境质量、监控企业的排污行为,通过环境数据的采集、传输、处理、整合,辅助环境管理决策。近年来环境物联网得到长足发展,但现有的环境物联网体系无法满足多种任务和应用场景的需求,主要问题体现在数据质量和精准度缺乏保证,数据传输能力有限,数据共享还未打通,数据分析应用能力还有待提升。笔者系统分析了环境物联网感知层、传输层和应用层的现状与问题,较全面地研究了物联网与大数据技术的最新进展,提出了环境物联网的发展方向以解决上述问题,为实现精细化管理与科学决策提供科学参考。     

A stochastic conflict resolution model for trading pollutant discharge permits in river systems

This paper presents an efficient methodology for developing pollutant discharge permit trading in river systems considering the conflict of interests of involving decision-makers and the stakeholders. In this methodology, a trade-off curve between objectives is developed using a powerful and recently developed multi-objective genetic algorithm technique known as the Nondominated Sorting Genetic Algorithm-II (NSGA-II). The best non-dominated solution on the trade-off curve is defined using the Young conflict resolution theory, which considers the utility functions of decision makers and stakeholders of the system. These utility functions are related to the total treatment cost and a fuzzy risk of violating the water quality standards. The fuzzy risk is evaluated using the Monte Carlo analysis. Finally, an optimization model provides the trading discharge permit policies. The practical utility of the proposed methodology in decision-making is illustrated through a realistic example of the Zarjub River in the northern part of Iran.     

填埋场衬层渗漏的电学法检测研究进展

综述了几种常用的填埋场渗漏检测方法,指出电学法已成为填埋场不同运行阶段（施工期和运营期）渗漏检测的主流方法。分析了各阶段电学检测方法的适用条件和优缺点,在防渗膜铺设阶段,常利用双电极法或电极-偶极子法进行施工完整性检测;在填埋场运营期间,根据场地实际情况可以选择电极格栅法、基于物联网的监测预警云平台、阵列式偶极子法或高密度电法进行膜渗漏检测及长期监测。     

Soft cost elements: Exploring management components of project costs in green building projects

This paper reveals the non-technical elements that affect construction project costs, known as soft cost elements (SCEs). These elusive elements have received little attention although they are significant in influencing management decisions of a project. There is much interest in the mitigation of cost for green projects, but the focus is generally leaning towards hard cost aspects. Information about SCEs remains obscure. Qualitative approach is adopted in this research using face-to-face unstructured interviews with 12 respondents consisting of green building project experts who have been involved in many projects certified by the Malaysia Green Building Index (GBI). A total of 4 project cost datasheets were also analysed to confirm the SCEs in the project budget and to enable a clear definition of the SCEs term. The results show that SCEs are tangible and not hidden in nature. Although they constitute smaller percentage in the project cost, the implications of SCEs are far-reaching and can influence critical management decisions. There are 8 SCEs identified which are divided into 3 main categories: 1) Design Influence (project brief preparation, consultant fees and green certification); 2) Authority Requirement (pre-development charges and contribution charges); and 3) Development Provision (marketing, project overheads and security of funding). This study is practical and relevant for both green and conventional construction project, with an additional of ‘green certification’ element for green projects. The paper provides new insight into the understated elements that drive project cost from a managerial perspective and indicates key areas for future studies on project cost.     

Real<Emphasis Type="Bold">-</Emphasis>time water quality monitoring using Internet of Things in SCADA

Water pollution is the root cause for many diseases in the world. It is necessary to measure water quality using sensors for prevention of water pollution. However, the related works remain the problems of communication, mobility, scalability, and accuracy. In this paper, we propose a new Supervisory Control and Data Acquisition (SCADA) system that integrates with the Internet of Things (IoT) technology for real-time water quality monitoring. It aims to determine the contamination of water, leakage in pipeline, and also automatic measure of parameters (such as temperature sensor, flow sensor, color sensor) in real time using Arduino Atmega 368 using Global System for Mobile Communication (GSM) module. The system is applied in the Tirunelveli Corporation (Metro city of Tamilnadu state, India) for automatic capturing of sensor data (pressure, pH, level, and energy sensors). SCADA system is fine-tuned with additional sensors and reduced cost. The results show that the proposed system outperforms the existing ones and produces better results. SCADA captures the real-time accurate sensor values of flow, temperature, and color and turbidity through the GSM communication.     

Assessment of potential impacts of municipal solid waste treatment alternatives by using life cycle approach: a case study in Vietnam

In Vietnam, most of municipal solid waste (MSW) is disposed of at open dumping and landfill sites, and the methane gas from waste is the un-ignorable source of greenhouse gas (GHG) emission. It is indispensable to explore the possibility for GHG mitigation in MSW management. The objective of this study was to estimate alternative waste treatment practices towards the GHG emission mitigation, energy consumption and generation, reduction of landfill volume, and various benefits for proposing the appropriate selection by scenario analyses for representative Vietnam’s cities. Impacts were calculated by utilizing life cycle assessment (LCA) method. A literature review survey on the current applicability of LCA database for assessing impacts from waste sector in developing countries, especially for Vietnam, was carried out. This study assessed the contribution of alternative solid waste treatment practices. The result showed that, except investment and operation costs, incineration with energy recovery seems the suitable alternative for treating waste from representative cities of Vietnam according to reduction of GHG emission and waste burden to landfill sites and energy recovery and generation. Besides, MSW composition was identified as an important factor directly influencing to impacts as well as other products and benefits of waste treatment alternatives. Reliable data on waste composition are indispensable for assessing to choose, improve, or plan the waste treatment practices towards sustainable development.     

城市绿色发展竞争力评价研究

提出城市绿色发展竞争力概念,探讨环境质量与经济社会发展水平之间的关系,从城市空气环境质量、地表水环境质量、声环境质量、集中式饮用水源地水质和生态环境质量等"硬环境"着手,结合衡量城市经济发展程度的工业GDP指标,构建评价指标体系,用单位工业GDP的环境代价来表征城市绿色发展竞争力水平,并以安徽省省辖市为例进行了实例研究。     

An Evaluation of Microscopic Emission Models for Traffic Pollution Simulation Using On-board Measurement

As a result of the continuously increasing numbers of motor vehicles in metropolitan areas worldwide, road traffic emission levels have been recognized as a challenge during the planning and management of transportation. Experiments were conducted to collect on-road emission data using portable emission measurement systems in two Chinese cities in order to estimate real traffic emissions and energy consumption levels and to build computational models for operational transport environment projects. In total, dynamic pollutant emissions and fuel consumption levels from dozens of light duty vehicles, primarily from four different vehicle classes, were measured at a second-by-second level. Using the collected data, several microscopic emission models including CMEM, VT-Micro, EMIT, and POLY were evaluated and compared through calibration and validation procedures. Non-linear optimization methods are applied for the calibration of the CMEM and EMIT models. Numerical results show that the models can realize performance levels close to the CMEM model in most cases. The VT-Micro model shows advantages in its unanimous performance and ability to describe low emission profiles while the EMIT model has a clear physics basis and a simple model structure. Both of them can be applied when extensive emission computation is required in estimating environmental impacts resulting from dynamic road traffic.