An Assessment of Airport Sustainability, Part 2—Energy Management at Copenhagen Airport

Reduction in energy usage has been investigated as the mechanism by which hypothermia provides protection during ischemia. We describe experiments using hypothermia in the rabbit retina in vitro that show a correlation between hypothermia-induced reductions in energy usage and neuroprotection. We examined energy metabolism and electrophysiological function under control/nonischemic conditions during 1 or 2 hours of "ischemia" (induced by decreasing glucose from 6 to 1 mmol/L and oxygen from 95 to 15%) and during 3 to 4 hours of "return-to-control" conditions. Glucose utilization and lactate production were measured as indices of energy metabolism, and light-evoked compound action potentials were monitored to assess functional recovery. Nonischemic retinas subjected to both mild (33 +/- 0.5 degrees C) and moderate (30 +/- 0.5 degrees C) hypothermia exhibited a decrease of 38% in the rate of glucose utilization and lactate production compared with normothermic retinas (36 +/- 0.5 degrees C) (analysis of variance, P < 0.001). In retinas that were made ischemic, mild or moderate hypothermia further reduced the rates of glucose utilization (18 and 39%, respectively) and lactate production (21 and 28%, respectively) (P < 0.001 for glucose, P < 0.01 for lactate). Retinas that had been mildly or moderately hypothermic during ischemia exhibited improved recovery of glucose utilization (65 and 57%, respectively) and lactate production (72 and 74%, respectively) compared with normothermic retinas (18% for glucose and 54% for lactate; repeated-measures analysis of variance, P < 0.001). Recovery of compound action potentials for retinas kept at 36, 33, and 30 degrees C was 15, 36, and 53%, respectively (repeated-measures analysis of variance, P < 0.001). Our studies in an avascular neuronal model of ischemia demonstrate that hypothermia protects against ischemic injury. We interpret the smaller reductions in energy generation and usage caused by ischemia when the retinas were hypothermic as evidence that hypothermia had reduced energy requirements more than energy production, and we propose that this at least in part explains its protection.

Harvesting energy from the environment can play an important role in reducing the dependency of an electronic system to primary energy sources (i.e. AC power or battery). For reliable and efficient energy harvesting while assuring best user experience, it is important to manage, route and match the harvested energy with the demand of various energy sources. In the most general case, multiple different energy sources can be used to provide energy to multiple different energy users. In this work, we propose a scalable rule-based energy management system for managing the acquisition, mixing, delivery and storage of energy for arbitrary collection of energy sources and users, which are characterized with different energy generation and consumption parameters. The system uses economics inspired supply-demand model for efficiently managing energy distribution between a set of energy sources and users. The energy allocation procedure tries to maximize the energy utilization efficiency of the sources while satisfying the demand of the users in order of their associated priorities, without starving an already allocated user. Simulation results for example scenarios show the effectiveness of the proposed approach for improving the energy utilization and lifetime of the energy sources.

The internet is providing a cost-effective method of providing energy users with valuable information that enables them to better control their energy usage. The days of relying solely on systems that are on-site to provide this information have passed. The ability of off-site energy managers to collect, process, and disseminate this information using internet based e-mail and web pages accessible by the end user's standard internet browser has become a much more attractive option. While centrally monitored energy management is not new, it has been used primarily by high-end users with dedicated staff to perform the gathering and analysis functions. With low-cost internet communications available, this service can now be provided to a much broader market. This market, consisting of K-12 school corporations, municipalities, medium-sized commercial office buildings, and modest-sized retail chains has been under-served by the energy management community, except in regions with high-cost energy rates. While energy retrofit activities have provided a significant amount of energy savings to much of this market, there are more savings to be had in the operational and system maintenance areas. The only information that many of these under-served markets have is monthly energy bills. They are generally used for billing only, though some energy cost savings can be achieved by analyzing them for mistakes or for rate change recommendations. When one tries to detect the cause of excess energy usage using monthly bills, inadequate results often happen because these bills are time-late and lack energy usage profile information needed to identify the cause of excess energy usage. This article provides information regarding the information systems necessary to manage a facility's energy usage. It demonstrates the need for better, near real-time energy usage information for energy engineers and facility managers. In providing useful, timely energy usage information, the energy engineer and facility manager are better able to manage the energy usage in the facilities.

The substantial discoveries of shale gas are leading to increasing attention of gas-to-liquid processes using Fischer–Tropsch chemistry. Traditionally, focus has been given to the reaction schemes, while major issues such as energy and water matters have been handled subsequently. There is a need to examine the impact of selecting the reforming technology on issues pertaining to sustainability such as energy and water usage. This paper analyzes energy and water generation and management options for three primary alternatives for the production of syngas: steam reforming, partial oxidation, and autothermal reforming. A combination of thermodynamic models and computer-aided simulation is used to quantify those aspects. Trade-offs are established for the use of a desired H2:CO ratio on water and energy usage. Also, systematic process integration techniques are used to identify the impact of energy and mass integration on the usage of energy and water in the process and to benchmark the process performance.

Wastewater treatment plants require substantial amounts of energy for pumping and treating water as well as for other plant operations. National and regional policy changes resulting in stricter water quality standards could result in an increase in energy requirements for treatment, as further treatment or more energy intensive technologies would be required. Given rises in energy costs, effective energy management plans are necessary for municipal wastewater treatment plants. Energy usage in wastewater treatment plants can be reduced through a number of different technology options without compromising water quality output. These different technology options vary in effectiveness, energy intensity, and upfront and operational costs, and the choice of technologies is often dependent upon local conditions and plant specific characteristics. Energy management can be improved also through electricity production on-site from the capture of methane gases released during treatment or from the inclusion of renewable electricity generating technologies. These technology options augment the creation of energy on-site while not necessarily reducing total energy demands. The objective of this paper is to, through use of a case study of a wastewater treatment facility in Boulder, Colorado, describe the most energy intensive processes of wastewater treatment, review various technology options for these processes, and discuss the opportunities and barriers to improving energy management at wastewater treatment plants. This paper is part of a topical session entitled “An Energy Appetite of U.S. Water Systems — what does it take to supply our water?”

In order to meet the energy and power requirements of large-scale battery applications, cells have to be connected in serial and parallel configuration. For the purpose of understanding the assembly of parallel-connected cells is referred to as logical cell. Caused by current and State of Charge (SoC) inhomogeneities between the serial and parallel cells, the usable power and energy will differ from the installed values. These differences are affected by the cell topology, cell parameter distributions and the current profile. The influences of these parameters are investigated by Monte Carlo simulations with Gaussian distributed cell parameters. The $5\sigma$ values of the usable power decrease almost logarithmically with increasing number of serial and parallel cells. The power is primarily limited by the logical cell with the highest current distribution, which in turn depends principally on the differences in cell parameters. In a battery the maximum difference of cell parameters statistically increase with the number of connected cells. Two further effects influence the usable energy. The Open Circuit Voltage (OCV) bending leads to a SoC balancing at the end of discharge. And the standard deviation of the logical cell capacity distribution decreases by square root with increasing number of parallel cells. These effetcs are leading to a higher usable energy by connecting the cells in parallel compared to a corresponding serial-connection, especially for discharging. Furthermore with increasing standard deviation of the cells resistance and capacity distributions a linear decrease of the usable power and energy is found.

The efficient management of energy usage in intelligent buildings presents a significant obstacle within sustainable infrastructure, requiring inventive approaches to tackle increasing energy requirements and environmental issues. Although Internet of Things (IoT) strategies produce a huge quantity of data in these structures, it is still challenging to extract practical insights for effective energy management. This study utilizes machine learning approaches, namely Long Short-Term Memory to Optimize Energy Consumption (LSTM-OEC), to tackle these difficulties in smart buildings using big data analytics. The suggested methodology combines historical energy usage data with real-time sensor information from IoT devices, driven by the need to cut energy expenses and decrease carbon emissions. Utilizing the LSTM-OEC model facilitates the integration of intricate temporal relationships and precise prediction of forthcoming energy requirements, hence facilitating the dynamic optimization of building energy systemsThe primary objectives of this research effort are to develop a preliminary energy management framework employing Long Short-Term Memory (LSTM) models, assess its effectiveness in reducing energy consumption, and examine its potential for scalability and suitability in real-world smart building environments. LSTM networks are selected because they can effectively process sequential data and acquire knowledge of long-term relationships. This characteristic renders them exceptionally well-suited for time-series projection tasks, such as the prediction of energy use. The work showcases the effectiveness of LSTM-based energy management systems in generating substantial savings in energy usage while ensuring occupant comfort levels through thorough testing and validation. The study results indicate that the LSTM-OEC model consistently performs better than conventional forecasting techniques, offering more precise projections of forthcoming energy demand. Furthermore, the findings of the study indicate that the suggested approach presents a viable and flexible resolution for enhancing energy efficiency in intelligent structures, hence facilitating the advancement of sustainable infrastructure.

Airports play a vital role in the air transport industry value chain, acting as the interface point between the air and surface transport modes. However, substantial volumes of waste are produced as a by-product of the actors’ operations. Waste management is therefore becoming especially important to airports. Using a qualitative and quantitative case study research approach, this paper has examined the waste management strategies and systems at Copenhagen Airport, Scandinavia’s major air traffic hub, from 1999 to 2016. The two major sources of waste at Copenhagen Airport are the waste generated from aircraft serving the airport and the waste arising from ground activities undertaken in the land and airside precincts. The growth in passengers and aircraft movements has had a concomitant impact on the volume of waste generated. Swept waste and sludge are processed by an external provider. Waste generated in the passenger terminals and the airport operator’s facilities is handled at a central container station, where it is sorted for incineration, recycling or for landfill. The environmental impact of the waste produced at the airport is mitigated through the recycling of waste wherever possible.

Fuzzy-based smart energy management system for residential buildings in Saudi Arabia: A comparative study

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

Energy demands are constantly increasing worldwide and forecasts estimate them to rise by approximately 44% worldwide by 2030 . At the same time traditional energy sources such as oil, coal gas, etc. diminish continuously, and consequently, there is a demand for renewable clean energy that is closely tied with a general need for more efficient energy production, usage and distribution strategies. In particular, the use of micro-power plants that are, for example, incorporated into smart homes or public buildings offer great potential by providing more dynamic yet dependable energy supply mechanisms. Nevertheless, in order to maximize such resources it is necessary to correlate energy demand with potential supply mechanisms efficiently in order to reduce energy overheads and to maximize the use of available resources in general. Moreover, it is necessary to take into account individual social as well as economic aspects in order to model the underlying environment and the dynamics thereof accurately. For that, new mechanisms are required that lead to intelligent and self-managing networks of knowledge that have the capability to self-organize energy management according to various operational, spatial and socio-economic aspects such as demand, costs, consumer preferences, business goals and others. From an Information and Communications Technology (ICT) perspective, this paper discusses some of the requirements of next-generation energy grids and identifies some of the tasks and challenges in this area that need to be addressed in order to realize infrastructures that are fully intelligent and are also aware of their social and economic parameters so that energy usage can be dynamically and autonomously controlled in relation to global demand and supply.

This thesis presents a methodology for energy management in large companies and its implementation through a web application and through a prototype of a simulation platform. By combining existing tools in an innovative manner and by making use of recent web technology developments, the methodology adopted provides engineers and managers with tools capable of guaranteeing an efficient and sustainable energy management. Although the methodology presented in this work is based on the experience acquired in the food industry, it can be easily applied in other industrial sectors. The methodology is based on two fundamental approaches commonly used to analyse energy consumption in industrial contexts: the top-down approach and the bottom-up approach. The top-down approach is used in the first place to identify the factories and the specific areas within the factories in which the largest improvement potentials can be achieved. In turn, the bottom-up approach builds on the results from the top-down approach to identify and quantify the energy saving potentials. The top-down approach is implemented through a web application in collaboration with an industrial partner. This application encompasses a modular factory model –accessible to engineers in factories through a user-friendly interface– which enables each factory to define its energy usage, allocate energy costs among the different energy consumers and compute key performance indicators. For a rational cost allocation in multi-service energy conversion units, an exergy-based methodology is presented. The efficiency of energy conversion units defined in the factory model, such as the boilerhouse or the air heaters, is assessed using thermodynamic models. The latter are simplified parametric models derived from accurate thermodynamic models developed in a general flow-sheeting and simulation software to comply with computation time and reliability requirements of the web application. The different factory models defined in the web application can be browsed as part of the proposed top-down approach: starting from a high level overview of the factory –targeted mainly at managers– users can then focus on a specific area of the factory. Strategies are developed to guide users in identifying factories or specific areas within the factories with the largest improvement potentials. They include the use of mechanism to rate the quality of a performance indicator as well as a benchmarking module that allows to compare performance indicators across factories worldwide. In sum, the modular and adaptive aspects of the web application guarantee its long-lasting use. In order to quantify energy saving potentials in the energy conversion units defined in a factory model, what if? scenarios are performed in a web-based simulation platform prototype developed in this thesis. This platform acts as a decision-support tool by providing graphical representations of profitability and risk analysis. The platform can be accessed by human users through a web browser while other applications, such as the web application described above, may use the simulation functions through a web service. Statistical tools that can help engineers in defining the factory model described above are also presented. They are used to correlate energy consumption with factors such as production volumes or the climate. Tests to validate the developed correlations are also described. The application of this technique in a factory shows that more than 50% of the energy consumption does not have a direct correlation with production factors and allows to identify improvement potentials. Finally, the concept of a bottom-up approach to identify and quantify energy saving potentials in the different production processes of a factory is presented. A triple representation of the requirements of a process is introduced and applied to process integration in a concrete example. The 80/20 rule is also applied to reduce the complexity of the problem. The optimal integration of cogeneration engines and heat pumps using multi-objective optimisation is also presented.

This article explores the transformative potential of hybrid energy systems and AI in revolutionizing traditional energy grids. Despite the prevalent use of fossil fuels, traditional grids operate inefficiently, generating energy continuously without responding to demand fluctuations. This article highlights the limitations of such systems and contrasts them with hybrid energy infrastructures that integrate solar panels and energy storage solutions for more dynamic and sustainable energy management. The article proposes several strategies for leveraging AI to optimize energy systems. By utilizing predictive analytics, AI can anticipate energy demands, optimize resource allocation, and reduce reliance on fossil fuels. This approach not only enhances grid stability and resilience but also facilitates the efficient use of renewable energy sources, such as solar power. Furthermore, AI empowers consumers with smart energy management tools, enabling informed decisions on energy usage that contribute to cost savings and environmental sustainability. The convergence of hybrid energy systems and AI presents a promising pathway towards a sustainable energy future. By implementing AI-driven predictive capabilities, stakeholders can unlock new opportunities for efficiency, resilience, and environmental stewardship in the energy sector.

King Mongkut’s University of Technology Thonburi (KMUTT) committed to be Sustainable University for SDGs 2030 since 2017 and committed to sustainability leadership in all of our activities from operations, teaching, to conducting research. Our commitments are to be a sustainable university providing a role model on Energy, Environment, Safety Management Systems and provide sustainability platform to promote sustainability leadership. Sustainable energy management system has been provided by top management policy since 2010 which focus on energy usage reduction, energy efficient appliances usage, renewable energy usage and carbon footprint reduction. To achieve the sustainable energy goal which comply to SDG 7: Affordable and Clean Energy and SDG13: Climate Action, KMUTT focus on student leadership which provide awareness raising, perception, and leading to implementation for all in KMUTT by using SEP for SDG concept. Moreover, KMUTT initiate sustainable strategy which provide the learning environment to make all campus as living lab, promote people participation and monitoring to make continual improvement with positive reinforcement. The sustainable energy goal achieved along with the expansion activities to community surround according to the student engagement. Keyword: energy management, sustainable energy, SEP for SDG, SDG 7, SDG 13

Deep fuzzy nets approach for energy efficiency optimization in smart grids