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Smart grid Lucas Nuelle

Rapport van S3 studenten die aan de Lucas Nuelle panelen hebben gewerkt met de Smart Grid module

An offshore energy simulation through flow networks: CEL within the MSP Challenge 2050 simulation game platform

This paper presents the design of the offshore energy simulation CEL as a flow network, and its integration in the MSP Challenge 2050 simulation game platform. This platform is designed to aid learning about the key characteristics and complexity of marine or maritime spatial planning (MSP). The addition of CEL to this platform greatly AIDS MSP authorities in learning about and planning for offshore energy production, a highly topical and big development in human activities at sea. Rather than a standard flow network, CEL incorporates three additions to accommodate for the specificities of energy grids: an additional node for each team's expected energy, a split of each node representing an object into input and output parts to include the node's capacity, and bidirectional edges for all cables to enable more complex energy grid designs. Implemented with Dinic's algorithm it takes less than 30ms for the simulation to run for the average amount of grids included in an MSP Challenge 2050 game session. In this manner CEL enables MSP authorities and their energy stakeholders to use MSP Challenge 2050 for designing and testing more comprehensive offshore energy grids.

Lessons learned bij projecten voor gebiedsgerichte inzameling van bedrijfsafval in Amsterdam

Final report – Johan Cruijff ArenA operational pilot

The Johan Cruijff ArenA (JC ArenA) is a big events location in Amsterdam, where national and international football matches, concerts and music festivals take place for up to 68,000 visitors. The JC ArenA is already one of the most sustainable, multi-functional stadia in the world and is realizing even more inspiring smart energy solutions for the venue, it’s visitors and neighbourhood. The JC ArenA presents a complex testbed for innovative energy services, with a consumption of electricity comparable to a district of 2700 households. Thanks to the 1 MWp solar installation on the roof of the venue, the JC ArenA already produces around 8% of the electricity it needs, the rest is by certified regional wind energy.Within the Seev4-City project the JC ArenA has invested in a 3 MW/2.8 MWh battery energy storage system, 14 EV charging stations and one V2G charging unit. The plan was to construct the 2.8 MWh battery with 148 2nd life electric car batteries, but at the moment of realisation there were not enough 2nd life EV batteries available, so 40% is 2nd life. The JC ArenA experienced compatibility issues installing a mix of new and second-life batteries. Balancing the second-life batteries with the new batteries proved far more difficult than expected because an older battery is acting different compared to new batteries.The EV-based battery energy storage system is unique in that it combines for the first time several applications and services in parallel. Main use is for grid services like Frequency Containment Reserve, along with peak shaving, back-up services, V2G support and optimization of PV integration. By integrating the solar panels, the energy storage system and the (bi-directional) EV chargers electric vehicles can power events and be charged with clean energy through the JC ArenA’s Energy Services. These and other experiences and results can serve as a development model for other stadiums worldwide and for use of 2nd life EV batteries.The results of the Seev4-City project are also given in three Key Performance Indicators (KPI): reduction of CO2-emission, increase of energy autonomy and reduction in peak demand. The results for the JC ArenA are summarised in the table below. The year 2017 is taken as reference, as most data is available for this year. The CO2 reductions are far above target thanks to the use of the battery energy storage system for FCR services, as this saves on the use of fossil energy by fossil power plants. Some smaller savings are by replacement of ICEby EV. Energy autonomy is increased by better spreading of the PV generated, over 6 instead of 4 of the 10 transformers of the JC ArenA, so less PV is going to the public grid. A peak reduction of 0.3 MW (10%) is possible by optimal use of the battery energy storage system during the main events with the highest electricity demand.

Increasing hosting capacity of low-voltage distribution network using smart charging based on local and dynamic capacity limits

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.

Can transformers transform financial forecasting?

Purpose This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series. Design/methodology/approach Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios. Findings The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023) Originality/value This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.