The SynergyS project aims to develop and assess a smart control system for multi-commodity energy systems (SMCES). The consortium, including a broad range of partners from different sectors, believes a SMCES is better able to incorporate new energy sources in the energy system. The partners are Hanze, TU Delft, University of Groningen, TNO, D4, Groningen Seaports, Emerson, Gain Automation Technology, Energy21, and Enshore. The project is supported by a Energy Innovation NL (topsector energie) subsidy by the Ministry of Economic Affairs.Groningen Seaports (Eemshaven, Chemical Park Delfzijl) and Leeuwarden are used as case studies for respectively an industrial and residential cluster. Using a market-based approach new local energy markets have been developed complementing the existing national wholesale markets. Agents exchange energy using optimized bidding strategies, resulting in better utilization of the assets in their portfolio. Using a combination of digital twins and physical assets from two field labs (ENTRANCE, The Green Village) performance of the SMCES is assessed. In this talk the smart multi-commodity energy system is presented, as well as some first results of the assessment. Finally an outlook is given how the market-based approach can benefit the development of energy hubs.
LINK
In this research, the experiences and behaviors of end-users in a smart grid project are explored. In PowerMatching City, the leading Dutch smart grid project, 40 households were equipped with various decentralized energy sources (PV and microCHP), hybrid heat pumps, smart appliances, smart meters and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid, several types of demand side management were tested. Households received feedback on their energy use either based on costs, or on the percentage of consumed energy that had been produced locally. Furthermore, devices could be controlled automatically, smartly or manually to optimize the energy use of the households. Results from quantitative and qualitative research showed that: (1) feedback on costs reduction is valued most; (2) end-users preferred to consume self-produced energy (this may even be the case when, from a cost or sustainability perspective, it is not the most efficient strategy to follow); (3) automatic and smart control are most popular, but manually controlling appliances is more rewarding; (4) experiences and behaviors of end-users depended on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.
Turbine blade cooling has been a topic of significant interest, as increasing turbine entry temperatures result in higher cooling requirements. The present numerical method divides the blade into a finite number of elements in the span and peripheral directions and solves the heat transfer fundamental equations for convection and conduction in both directions. As inputs, the span and chord gas temperature and heat transfer coefficient distributions are required. The results include high resolution temperature prediction for the blade and coolant, at all span and chord positions. The advantages of the method include the capturing of blade temperature variation in all directions, while considering the thermal diffusion due to conduction. Mach number effects to the resulted blade and coolant temperature are highlighted, as local distribution of the gas static temperature can have a dominant role. The effect of averaging the input parameters to the predicted blade temperature is discussed and finally, different values for the material conductivity are simulated and the results are analysed.
MULTIFILE
