In many cities, pilot projects are set up to test or develop new technologies that improve sustainability, urban quality of life or urban services (often labelled as “smart city” projects). Typically, these projects are supported by the municipality, funded by subsidies, and run in partnerships. Many projects however die after the pilot stage, and never scale up. Policymakers on all levels consider this as a challenge and search for solutions. In this paper, we analyse the process of upscaling, focusing on smart city projects in which several partners –with different missions, agenda’s and incentives- join up. First, we review the extant literature on upscaling from development studies, business studies, and the transition management literature. Based on insights from these literatures, we identify three types of upscaling: roll-out, expansion and replication, each with their own dynamics, context sensitivity and scaling barriers. We illustrate the typology with recent smart city projects in Amsterdam. Based on desk research and in-depth interviews with a number of project stakeholders and partners of the Amsterdam Smart City platform, we analyse three projects in depth, in order to illustrate the challenges of different upscaling types. i) Energy Atlas, an EU-funded open data project in which the grid company, utilities and local government set up a detailed online platform showing real-time energy use on the level of the building block; ii) Climate Street, a project that intended to make an entire urban high street sustainable, involving a large number of stakeholders, and iii) Ikringloop, an application that helps to recycle or to re-use waste. Each of the projects faced great complexities in the upscaling process, albeit to a varying degree. The paper ends with conclusions and recommendations on pilot projects and partnership governance, and adds new reflections to the debates on upscaling.
While the Municipality of Amsterdam wants to expand the electric vehicle public charging infrastructure to reach carbon-neutral objectives, the Distribution System Operator cannot allow new charging stations where low-voltage transformers are reaching their maximum capacity. To solve this situation, a smart charging project called Flexpower is being tested in some districts. Charging power is limited during peak times to avoid grid congestion and, therefore, enable the expansion of charging infrastructure while deferring grid investments. This work simulates the implementation of the Flexpower strategy with high penetration of electric vehicles, considering dynamic and local power limits, to assess the impact on both the satisfaction of electric vehicle users and the business model of the Charging Point Operator. A stochastic approach, based on Gaussian Mixture Models, has been used to model different profiles of electric vehicle users using data from the Amsterdam public electric vehicle charging infrastructure. Several key performance indicators have been defined to assess the impact of such charging limitations on the different stakeholders. The results show that, while Amsterdam’s existing public charging infrastructure can host just twice the current electric vehicle demand, the application of Flexpower will enable the growth in charging stations without requiring grid upgrades. Even with 7 times more charging sessions, Flexpower could provide a power peak reduction of 57% while supplying 98% of the total energy required by electric vehicle users.
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.
