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A Regional Civilian Airport Model at Remote Island for Smart Grid Simulation
Abstract
The purpose of this study is to design and implement a scientific tool which will be used to investigate the application of smart grids in the aviation industry and to evaluate the proof of concept. A case study for a regional Greek airport is proceeded with the development of a co-simulation agent-based model which includes building and electrical system simulation, climate data, flights, and passengers’ flow. In terms of methodology, the load types and schedule will be studied, like HVAC, building and runway lighting. It is presented how passengers fluctuation affects each type of load and, as a result, energy consumption throughout different hours of the day, depending on weather conditions. After the collection and validation of the above data, a model of each type of load at a typical regional civilian airport will be created. Finally, the models will be used with software packages and the co-simulation framework. The produced results are evaluated and are presented.
... Beyond the electricity data that was studied in this paper, the specific application could be applied both to financial data and meteorological phenomena as wind power forecasting [40]. Additionally, this application could be utilized in the aviation industry with a combination of a scientific tool which incorporates building and electrical system simulation, climate data, flights, and passengers' flow [41]. ...
Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.
... Electrical load forecasting and power generated from different renewable energy sources is extremely important to ensure the performance and effectiveness of all power networks [3][4][5]. This sector is generally classified in terms of forecasting load as follows: very short-term load forecasting (VSLTF) for a few minutes, short-term load forecasting (SLTF) ranging from one hour to one week ahead, mid-term load forecasting (MLTF) for more than one week to a few months and long-term load forecasting (LTLF) for longer than one year [6]. ...
In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability.
Smart Airport concept is the future of Airport operation and it may dramatically change the industry towards modern technology adaptation. This study mainly focuses on the Smart airport applications in to passenger terminal process. Literature scattered on different sources have been summarised to explain the features of smart airport with practical cases in global context. Empirical indications on implementation of the smart airport applications discussed based on practical implications of airport operations. Abstract-Smart Airport concept is the future of Airport operation and it may dramatically change the industry towards modern technology adaptation. This study mainly focuses on the Smart airport applications in to passenger terminal process. Literature scattered on different sources have been summarised to explain the features of smart airport with practical cases in global context. Empirical indications on implementation of the smart airport applications discussed based on practical implications of airport operations. Special
Describing the location of an airport within a region, the vocabulary
of urban studies is often dominated by ill-defined terms such
as urban fringe, centre, suburb, corridors, etc. The dataset presented
by this manuscript aims to provide a basis to describe and
compare the location of 76 major European airports within their
respective urban regions. The dataset consists of seven types of
data: Betweenness centrality of major roads at 45 km radius of
each airport region, population density, distribution of urbanized
areas, location of agricultural lands, location of the natural area,
and distribution of leisure and industrial sites. Ultimately,
employing hierarchical clustering, five typologies of the European
airport regions, given the regional location of airport, are identified:
(1) Urban airports; (2) Urban periphery airports; (3)
Agricultural-area airports; (4) Natural-area airports; (5) Remote
airports.
This paper focusses upon passenger flow issues within airport terminals and includes all activities occurring between curb-side and boarding. To improve passenger flow and associated planning activities, a simulation framework is developed using Discrete-Event Simulation (DES). The DES is built using ExtendSim V9.2 simulator software from Imagine That. The model can be used to evaluate the efficiency of the outbound operational processes including check-in, security screening, immigration & custom and boarding. It can also assist management to identify potential bottlenecks in the system. The main input of the model is the flight schedule. A case study of the Brisbane international airport was analysed.
The main function of an airport is to provide access to air transport both for passengers and cargo. The number of air operations over the past 20 years has increased rapidly, and this has led to a rise in the energy needs of airports to satisfy this demand. As a consequence, the cost of energy supply for airport managers has escalated. At the same time, global energy consumption has soared due to the needs of emerging countries like China and India, with the consequent environmental impact. This complex scenario of environmental and economic factors has made airport managers become aware of the need to reduce energy consumption as well as a more efficient use of it. The aim of this article is to analyze the main behaviors and energy trends at airports in more recent research, starting with the description of the main energy sources and consumers, the application of energy conservation and energy efficiency measures, the establishment of energy indicators and benchmarking between airports, as well as energy modeling and simulation.
Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.
Nowadays, airports' interest in solar photovoltaics (PVs) is increasing. It is a way to lower the burden of energy costs and to show environmental stewardship. This paper aims to study the application of PVs in the airport environment. Solar projects in airport areas across the world were studied to find the techno‐economic and environmental aspects of airport‐based solar PV application. The favorable factors for solar PV are observed to be effective land utilization, low height profile, versatility, flexibility, and mature silicon PV technology. The major considerations for airport‐based solar PV applications were glare occurrence, radar interference, and penetration into airspace. Airport's weather and soil conditions, selection of PV technology, glare impact, site selection, and grid interconnection issues were the technical challenges. It was observed that economic factors such as reduced energy cost, life cycle cost, and sustainability grants and incentives make solar projects in airports attractive. Carbon mitigation and achievement of sustainability goals through solar PV installation emphasized the environmental stewardship of airports. From case studies, it was understood that prior planning, glare assessment, and careful implementation are essential for the success of airport‐based solar PV farms. Airport‐based solar PV has immense scope in the near future. This paper aims to study the application of photovoltaics (PVs) in the airport environment. The major considerations for airport‐based solar PV application are glare occurrence, radar interference, and penetration into airspace. It was observed that economic factors such as reduced energy cost, life cycle cost, and sustainability‐focused grants and incentives make solar projects in airports attractive. Carbon mitigation and corresponding achievement of sustainability goals through solar PV installation emphasized the environmental stewardship of airports.
This study investigates the determinants of air traffic volumes and structure (carrier type) at lesser studied small
European airports serving below one million passengers per annum. It discusses airport choice factors for airline
customers and focuses on the characteristics of small airports and their catchment areas in order to explain
variation in 2016 traffic volumes for 146 regional gateways in 21 EU countries. Through the application of
multiple linear regression and correlation it is found that population size, airport charges and the need for
capacity coordination are the most related to the number of passengers using these airports. Several different
correlations are found with respect to the share of low-cost, full-service and charter carriers as a proportion of
total airport traffic, which in most cases extends the findings of the limited number of other studies in the field of
small regional airports. The research concludes that while certain relationships can be found, their statistical
significance is moderate, leaving scope for a deeper study into a smaller samples of airports.
“Self-connection” involves an approach whereby travelers choose to buy two or more separate tickets and transfer at intermediate airports by themselves. While self-connection markets are growing around the world, some airports such as London Gatwick and Milan Malpensa have introduced special services to assist self-connecting passengers. In order to understand the potential opportunities of self-connectivity for Asian airports, this research applies the HCPI (Hub Connectivity Performance Index) and HEI (Hub Efficiency Index) to measure the connectivity at major Asian airports. The Boston Consulting Group (BCG) matrix is used to analyze the market position and future strategy of each airport. The results reveal that Singapore (SIN), Tokyo Narita (NRT), Kuala Lumpur (KUL) and Seoul Incheon (ICN) are the largest potential transfer airports for LCC self-connecting passengers and need to increase their LCC self-connectivity growth rates to become stars in this market. Flights between the Asia Pacific and North America are potential routes for NRT and ICN to develop LCC self-connectivity, while flights between the Asia Pacific and Europe are potential routes for SIN and KUL to develop such connectivity.
This paper describes a conceptual model intended to be applied in a general approach to the micro-simulation of hub airports terminals. The proposed methodology is illustrated with the development of a simulation model originally intended to help in the design of the new terminal at Barcelona International Airport. This model represents in detail, among many other elements, passengers’ flows in the different areas of these complex facilities. Agent-based simulation techniques were included to represent the different actors’ behaviors, and a formal representation of the model using Specification and Description Language (SDL) was used to represent the complexity of all the system elements. To pre-process a diverse and considerable amount of raw data provided by airport designers and other sources to feed the simulation environment Flight Planner Manager was developed as a toolkit to parameterize the different model factors and to generate required specific input data. This project was conducted over 3 years leading to the development of a system not only conceived to assess in the airport initial design process but also to constitute a recurrent decision taking instrument to dynamically optimize terminal management and operations.
The need for airports to optimise space and the need for airlines to bring down their operating costs favours the use of self-service technologies in services provided to passengers and at check-in, specifically. Checking in online and at kiosks in the airport is gaining ground on the use of the airline check-in desk. The objective of this paper is to analyse the socio-demographic factors or flight characteristics that influence a passenger’s choice of check-in from the various options available. For this a multinomial logit is used and applied to an extensive sample of almost 20,000 passengers, of whom 43% were foreigners, at five Spanish airports. The factors that determine the choice of check-in mode include the passenger’s age and level of education, the reason for making the journey, waiting time and the type of airline. The universal use of the new technologies in airport management, and the broad cosmopolitan sample mean that the conclusions can be easily extrapolated to other airport systems.
It is predicted that the twenty-first century will be dominated by air transport, both for domestic and international carriage of passengers and cargo. Thus the airport, as a driver of regional growth, is expected to become more than merely a regional gateway. Rather, it will function as city in itself, with living spaces for workers and their families, factories relying on airborne inputs and service industries located around the airport, with major road and rail infrastructure connected to it. However, the ‘aerotropolis’, as this hub for industry and driver of economic development has been called, has not yet been critiqued adequately, especially from a long-term public policy and planning perspective. This article raises concerns about three different dimensions to the aerotropolis regarding its long-term sustainability, viz., energy provision, the security of critical infrastructure and export pathways. In particular, this article argues that air transport will not replace existing components of international economic development. The authors contend that the three dimensions need to be explored in order to arrive at a more balanced view of the aerotropolis and its place in an increasingly complex global future.
An entire airport terminal building is simulated to examine passenger flows, especially international departures. First, times needed for passengers to be processed in the terminal building are examined. It is found that the waiting time for check-in accounts for more than 80 percent of the total waiting time of passengers spent in the airport. A special-purpose data-generator is designed and developed to create experimental data for executing a simulation. It is found that the possible number of passengers missing their flights could be drastically reduced by adding supporting staff to and by making use of first- and business-class check-in counters for processing economy- and group-class passengers.
Smart flight security in airport using IOT (Case study: airport of Birjand)
- R Jalali
- S Zeinali