Standard mass-production is a well-known manufacturing concept. To make small quantities or even single items of a product according to user specifications at an affordable price, alternative agile production paradigms should be investigated and developed. The system presented in this article is based on a grid of cheap reconfigurable production units, called equiplets. A grid of these equiplets is capable to produce a variety of different products in parallel at an affordable price. The underlying agent-based software for this system is responsible for the agile manufacturing. An important aspect of this type of manufacturing is the transport of the products along the available equiplets. This transport of the products from equiplet to equiplet is quite different from standard production. Every product can have its own unique path along the equiplets. In this article several topologies are discussed and investigated. Also, the planning and scheduling in relation to the transport constraints is subject of this study. Some possibilities of realization are discussed and simulations are used to generate results with the focus on efficiency and usability for different topologies and layouts of the grid and its internal transport system. Closely related with this problem is the scheduling of the production in the grid. A discussion about the maximum achievable load on the production grid and its relation with the transport system is also included.
Author supplied: In a production environment where different products are being made in parallel, the path planning for every product can be different. The model proposed in this paper is based on a production environment where the production machines are placed in a grid. A software entity, called product agent, is responsible for the manufacturing of a single product. The product agent will plan a path along the production machines needed for that specific product. In this paper, an optimization is proposed that will reduce the amount of transport between the production machines. The effect of two factors that influence the possibilities for reductions is shown in a simulation, using the proposed optimization scheme. These two factors are the redundancy of production steps in the grid and the
This study used historical data from a Park & Ride facility in Amsterdam to build a validated computer (Python) model to optimize battery and grid connection sizing. The case study modelled is equipped with 8 EV chargers (16 connections), an on-site supplementary battery, and a limited capacity grid connection. This model was then used to optimize the battery energy storage capacity and grid connection capacity for minimal annualized investment, using a future proof monthly load profile. A variety of battery control strategies were simulated using both the optimal system sizing and the current system sizing. The results were compared and a recommended control strategy presented, considering a number of performance metrics.
MULTIFILE
A fast growing percentage (currently 75% ) of the EU population lives in urban areas, using 70% of available energy resources. In the global competition for talent, growth and investments, quality of city life and the attractiveness of cities as environments for learning, innovation, doing business and job creation, are now the key parameters for success. Therefore cities need to provide solutions to significantly increase their overall energy and resource efficiency through actions addressing the building stock, energy systems, mobility, and air quality.The European Energy Union of 2015 aims to ensure secure, affordable and climate-friendly energy for EU citizens and businesses among others, by bringing new technologies and renewed infrastructure to cut household bills, create jobs and boost growth, for achieving a sustainable, low carbon and environmentally friendly economy, putting Europe at the forefront of renewable energy production and winning the fight against global warming.However, the retail market is not functioning properly. Many household consumers have too little choices of energy suppliers and too little control over their energy costs. An unacceptably high percentage of European households cannot afford to pay their energy bills. Energy infrastructure is ageing and is not adjusted to the increased production from renewables. As a consequence there is still a need to attract investments, with the current market design and national policies not setting the right incentives and providing insufficient predictability for potential investors. With an increasing share of renewable energy sources in the coming decades, the generation of electricity/energy will change drastically from present-day centralized production by gigawatt fossil-fueled plants towards decentralized generation, in cities mostly by local household and district level RES (e.g PV, wind turbines) systems operating in the level of micro-grids. With the intermittent nature of renewable energy, grid stress is a challenge. Therefore there is a need for more flexibility in the energy system. Technology can be of great help in linking resource efficiency and flexibility in energy supply and demand with innovative, inclusive and more efficient services for citizens and businesses. To realize the European targets for further growth of renewable energy in the energy market, and to exploit both on a European and global level the expected technological opportunities in a sustainable manner, city planners, administrators, universities, entrepreneurs, citizens, and all other relevant stakeholders, need to work together and be the key moving wheel of future EU cities development.Our SolutionIn the light of such a transiting environment, the need for strategies that help cities to smartly integrate technological solutions becomes more and more apparent. Given this condition and the fact that cities can act as large-scale demonstrators of integrated solutions, and want to contribute to the socially inclusive energy and mobility transition, IRIS offers an excellent opportunity to demonstrate and replicate the cities’ great potential. For more information see the HKU Smart Citieswebsite or check out the EU-website.
Making buildings smarter will save energy and make energy systems more flexible to address grid congestion. This is done by adding smart functionalities (such as machine learning and AI) to existing building management systems and by making full use of building data. Applied research and innovation on smart buildings is urgently needed to evaluate the best smart solutions for buildings applicable to different types of buildings across different contexts, and to assess their costs and benefits. Research on smart buildings, therefore, plays a large role in European, national and regional R&I agenda’s on energy, climate and digitalisation. Amsterdam University of Amsterdam (AUAS) has a growing research group on building energy management and smart buildings, supporting the sustainable transition of its own campus and the Amsterdam region. However, to date, AUAS has not been able to engage in international research projects in this area. Recently, AUAS became a partner in an European University Alliance (U!REKA European University), U!REKA comprises of six universities of applied sciences across Europe with its mission focusing on climate neutral communities and cities. Several partners with U!REKA are also conducting research on smart buildings and smart campuses, but, like AUAS, still in relative isolation. U!REKA will provide the collaboration framework for future joint research to be kick-started by the proposed SIA pilot project. In this research project, AUAS will cooperate with the Technical University Eindhoven, Metropolia University of Applied Sciences (Helsinki) and Politecnico de Lisboa (Lisbon) as consortium partners. Supporting partners are Frankfurt University of Applied Sciences, KTH Royal Institute of Technology (Stockholm) and TVVL (Dutch knowledge platform and association of professionals in the installation sector). The research is based on smart building case studies on the campuses of the project partners.
