• Energy Research
• 2021-2025close
• Open Access close
• DE close
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This study introduces novel phenothiazine-based organic dyes, 2-LBH-100, 2-LBH-44, and 2-Ryu-4, specifically designed for quasi-solid-state dye-sensitized solar cells (QsDSSCs). Employing a donor-π-acceptor architecture, these dyes incorporate varying electron-donating moieties, including bis(3-(hexyloxy)phenyl)amine and diphenylamino, coupled with a cyanoacrylic acid acceptor. Alkoxy substitutions in 2-LBH-100 and 2-LBH-44 enhanced solubility and dye loading on TiO2, leading to improved performance in QsDSSCs. 2-LBH-100 exhibited a power conversion efficiency (PCE) exceeding 5% with excellent stability, while 2-LBH-44 demonstrated a PCE of over 3%, increasing to 4% over time. 2-Ryu-4, with its diphenylamino donor, achieved an initial PCE of over 6%. This research highlights the crucial role of donor–acceptor interactions in optimizing organic dye design for high-performance QsDSSCs, paving the way for efficient and stable next-generation solar energy technologies.

This deliverable includes the methodology in EnergyPROSPECTS for an in-depth study of energy citizenship. It features the criteria used for selecting the cases for indepth study, the list of cases selected for in-depth study as well as key research foci and empirical research questions.

The disposal area of a deep geological repository (DGR) for the disposal of spent nuclear fuels (SNFs) is estimated considering the spacing between deposition holes and between disposal tunnels, as determined by a thermal analysis using the decay heat of a reference SNF. Given the relatively large amount of decay heat of the reference SNF, the disposal area of the DGR is found to be overestimated. Therefore, we develop a computer program using MATLAB, termed ACom (Assembly Combination), to combine SNFs when stored in canisters such that the decay heat per canister is evenly distributed. The stability of ACom was checked and the overall distribution of the decay heat per canister was analyzed. Finally, ACom was applied to disposal scenarios suggested in the conceptual design of a DGR for SNFs, and it was confirmed that the decay heat per canister could be evenly distributed and that the maximum decay heat of the canister could be much lower than that of a canister estimated using a reference SNF. ACom can be used to improve the disposal efficiency by reducing the disposal area of a DGR for SNFs by ensuringg a relatively even distribution of decay heat per canister.

Research on digital twins (DTs) in the power system field has mainly focused on implementing DTs for specific resources, while few studies on electric vehicle (EV)-based DT implementation have considered integration and interoperability between systems. This study introduces a DT-based EV system operation framework to address the aforementioned research gap. The framework implements individual EVs, charging stations, and charging station operators (CPOs) as DTs, enabling integrated operation with the power grid. The DT-based EV agent supports independent decision-making on power service participation by considering location information, distance, charging amount, spare time, and incentives. In addition, the CPO can establish an optimal incentive strategy to induce EV users to participate in grid power services. The proposed DT systems map information between EVs, charging stations, and the grid, enabling analysis and verification of the impact of participants on charging station operation, grid stability, and economic efficiency in an independent environment. The effectiveness and usability of the proposed framework were verified through a case study on an incentive-based demand response program.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Installations of an Energy Storage System (ESS) with various functions such as power stabilization of renewable energy, demand management, and frequency adjustment are increasing. In particular, ESS for demand management is being established for high-voltage customers (300 KVA–1000 KVA) who have placed an Auto Section Switch (ASS) at the connection point within the distribution system. However, a power outage may occur in the Power Receiving System (PRS) when a short-circuit fault due to insulation breakdown occurs at the ESS DC side. The reason for this breakdown is that the fault current is reduced by transformer impedance, and the ASS is opened before the DC power fuse. Therefore, using the Graphic Solution Method (GSM), this paper presents an operation algorithm for protection coordination that isolates the fault section by first operating the DC power fuse with a small fault current. Furthermore, fault analysis modeling for a PRS composed of a switchgear section, a main distribution panel, a Power Conditioning System (PCS), a power fuse, and a battery is performed through PSCAD/EMTDC. From the simulation results, it is confirmed that the fault section is quickly isolated, and power outages for high-voltage customers are prevented because the DC power fuse selected by the proposed operation algorithm of protection coordination is opened before the ASS.

Mixing under high pressure conditions plays a central role in several engineering applications, such as direct-injection engines and liquid rocket engines. Numerical flow simulations have become a complementary tool to study the mixing process under these conditions but require complex thermodynamic modeling as well as validation with accurate experimental data. For this reason, we use experiments of supercritical single-phase jet mixing from the literature, where the mixing is quantified by the mixture speed of sound, as a reference for our work. We here focus on the thermodynamic modeling of multi-component flows under high pressure conditions and the analytical calculation of the mixture speed of sound. Our thermodynamic model is based on cubic equations of state extended for multi-components. Using an extension of OpenFOAM, we perform large-eddy simulations of hexane and pentane injections and compare our results with the experimentally measured mixture speed of sound at specific positions. The simulation results show the same characteristic trends, indicating that the mixing effects are well reproduced in the simulations. Additionally, the effect of the sub-grid scale modeling is assessed by comparing results using different models (Smagorinsky, Vreman, and Wall-Adapting Local Eddy-viscosity). The comprehensive simulation data presented here, in combination with the experimental data, provide a benchmark for numerical simulations of jet mixing in high pressure conditions.