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Applied energy 388, 125530 (2025). doi:10.1016/j.apenergy.2025.125530 Published by Elsevier Science, Amsterdam [u.a.]

Luo Ji-Wang, Yaji Kentaro, Chen Li, et al. Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage. Applied Energy 377, 124596 (2024); https://doi.org/10.1016/j.apenergy.2024.124596. ; This work develops a data-driven multi-fidelity topology design (MFTD) method for designing fins in a latent heat thermal energy storage tube. The high-fidelity simulation resolves the actual solid-liquid phase change process using the enthalpy method, while the low-fidelity topology optimization (TO) simply considers the natural convection with Darcy flow. The above MFTD method is integrated into the framework of evolutional algorithm, and the variational autoencoder is introduced to generate new offspring. Fins for accelerating the melting and solidification processes at different Grashof number (Gr) are designed. It is found that when the fin volume fraction is low, the melt designs exhibit strong heterogeneity due to the strong convection, while the solidification designs are almost isotropic. Along with the increase of the fin volume fraction, the fins are first getting longer, then having more branches or sub-branches and finally becoming thicker. The superiority of the present data-driven MFTD method to the gradient-based direct TO method for solving optimization problems with strong multimodality has been demonstrated in this work. Results find that compared with the designs from direct TO, the MFTD melt design can further reduce the melting time by at least 27 % and 20 % at Gr = 3.3 × 103 and Gr = 3.3 × 104 respectively, and the MFTD solidification design can further shorten the solidification time by at least 9 %.

A realistic pursuit of decarbonization targets requires planning and designing new configurations of “multi-energy systems” to identify the optimal number, type, location and size of the energy conversion and storage units and their interconnections with the end users of different forms of energy. The common approach in the literature is to treat the optimization problem of energy conversion and storage separately from that of energy networks, and the few attempts to address the two problems simultaneously have led to oversimplifications due to the very large number of decision variables involved. To fill this gap, this study introduces “DOMES” (Design Of Multi-Energy Systems), a general optimization method for the integrated synthesis, design and operation of a multi-energy system in its entirety. With the goal of minimizing costs and reducing carbon emissions, DOMES can simultaneously find the location, type, size and operation of the energy conversion and storage units, as well as t...

Operating a reliable electricity system requires strict safety and security criteria such as avoiding grid congestion, minimum levels of inertia, maintaining voltage levels, and minimum adequacy reserves. However, large scale integration of intermittent renewables, namely inverter-based resources (IBR), is creating operational challenges. When operational security criteria are not met, system operators use ancillary services (redispatching) to activate or curtail scheduled units to manage the flows. In Spain, the volumes and costs of redispatching have multiplied by two and nine times between 2019 and 2023, respectively. In 2023, the total costs amounted to 2 b€, for curtailing 3 TWh wind and 0.9 TWh photovoltaics. A similar picture is emerging in other countries. This is the first study to examine the determinants of network constraints associated with redispatched volumes at country level. We use the seasonal autoregressive ARIMA time-series estimators with hourly operational and market data (2019–2023). Results show that actions to alleviate grid bottlenecks amount to one-third of the volumes, and increasing every year with addition of wind, photovoltaics and thermosolar generation. Volumes for solving voltage issues (reactive energy needs) represent one-half. Scheduled MWh from IBR (wind and photovoltaics) increases volumes for voltage problems (+0.05 MWh) and congestion issues (+0.01 MWh). Scheduled MWh from CHP contributes to congestion issues (+0.18 MWh), while thermosolar to grid reliability (N-1) problems (+0.25 MWh). The day-ahead and intraday markets can make economically efficient allocation of units, but massive connection of renewables requires increasing actions by system operators. Operational and regulatory decisions must be taken in advance to avoid the issue in the future.

A significant challenge is to determine the specific services Battery Energy Storage System (BESS) should provide to maximize profits. This study investigates the most profitable markets and sizes of BESS with utility-scale solar Photovoltaics (PV) power plants using techno-economic analysis frameworks. The objective is to maximize profitability in energy and frequency markets, focusing on primary regulation and day-ahead markets for Sweden and Germany. The inputs are historical market prices and frequency data, as well as real measurement PV power data. The results show that adding a BESS to an existing PV park does not result in a lower payback period than if implementing a stand-alone BESS. However, the payback period differs between Sweden and Germany during 2023, i.e., being 1.8 and 6.8 years, respectively. This is explained by the lower frequency market prices for Germany compared to Sweden. The technical results indicate that the BESS energy capacity after 10 years of operation is approximately 83% for Germany, whereas, for Sweden, it is around 87%. Also, combining the operating of BESS on primary regulation and day-ahead markets showed a 6-year payback period with a slight increase in loss of energy capacity (from 83 to 80%) for Germany. Moreover, combining various PV-BESS sizes showed a discrepancy in economic and technical metrics for the BESS in Germany, resulting in a best-case of a 6-year payback period. A sensitivity analysis, which examines a drop in the frequency control prices in the future relative to 2023 (by 20% and 50% for Germany and Sweden, respectively), reveals an increase in the payback period for both countries by approximately 1 year.