Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economic-environmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services.
The ever-increasing electrification of society has been a cause of utility grid issues in many regions around the world. With the increased adoption of electric vehicles (EVs) in the Netherlands, many new charge points (CPs) are required. A common installation practice of CPs is to group multiple CPs together on a single grid connection, the so-called charging hub. To further ensure EVs are adequately charged, various control strategies can be employed, or a stationary battery can be connected to this network. A pilot project in Amsterdam was used as a case study to validate the Python model developed in this study using the measured data. This paper presents an optimisation of the battery energy storage capacity and the grid connection capacity for such a P&R-based charging hub with various load profiles and various battery system costs. A variety of battery control strategies were simulated using both the optimal system sizing and the case study sizing. A recommendation for a control strategy is proposed.
Modern airport management is challenged by the task of operating aircraft parking positions most efficiently while complying with environmental policies, restrictions, schedule disruptions, and capacity limitations. This study proposes a novel framework for the stand allocation problem that uses a divide-and-conquer approach in combination with Bayesian modelling, simulation, and optimisation to produce less-pollutant solutions under realistic conditions. The framework presents three innovative aspects. First, inputs from the stochastic analysis module are used in a multivariate optimisation for generating variability-robust solutions. Second, a combination of optimisation and simulation is used to finely explore the impact of realistic uncertainty uncaptured by the framework. Lastly, the framework considers the role of human beings as the final control of operational conditions. A case study is presented as a proof of concept and demonstrates results achievable and benefits of the framework proposed. The experimental results demonstrate that the framework generates less-pollutant solutions under realistic conditions.
In the past decade additive manufacturing has gained an incredible traction in the construction industry. The field of 3D concrete printing (3DCP) has advanced significantly, leading to commercially viable housing projects. The use of concrete represents a challenge because of its environmental impact and CO2 footprint. Due to its material properties, structural capacity and ability to take on complex geometries with relative ease, concrete is and will remain for the foreseeable future a key construction material. The framework required for casting concrete, in particular non-orthogonal geometries, is in itself wasteful, not reusable, contributing to its negative environmental impact. Non-standard, complex geometries generally require the use of moulds and subsystems to be produced, leading to wasteful, material-intense manufacturing processes, with high carbon footprints. This research proposal bypasses the use of wasteful scaffolding and moulds, by exploring 3D printing with concrete on reusable substructures made of sand, clay or aggregate. Optimised material depositing strategies for 3DCP will be explored, by making use of algorithmic structural optimisation. This way, material is deposited only where structurally needed, allowing for further reduction of raw-material use. This collaboration between Neutelings Riedijk Architects, Vertico and the Architectural Design and Engineering Chair of the TU Eindhoven, investigates full-scale additive manufacturing of spatially complex 3D-concrete printed components using multi-material support systems (clay, sand and aggregates). These materials can be easily shaped multiple times into substrates with complex geometries, without generating material waste. The 3D concrete printed full-scale prototypes can be used as lightweight façade elements, screens or spatial dividers. To generate waterproof components, the cavities of the extruded lattices can be filled up with lightweight clay or cement. This process allows for the exploration of new aesthetic, creative and circular possibilities, complex geometries and new material expressions in architecture and construction, while reducing raw-material use and waste.
