Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources

The penetration of renewable energy sources (RESs) is increasing in modern power systems. However, the uncertainties of RESs pose challenges to distribution system operations, such as RES curtailment. Demand response (DR) and battery energy storage systems (BESSs) are flexible countermeasures for distribution-system operators. In this context, this study proposes an optimization model that considers DR and BESSs and develops a simulation analysis platform representing a medium-sized distribution system with high penetration of RESs. First, BESSs and DR were employed to minimize the total expenses of the distribution system operation, where the BESS model excluding binary state variables was adopted. Second, a simulation platform based on a modified IEEE 123 bus system was developed via MATLAB/Simulink for day-ahead scheduling analysis of the distribution system with a high penetration of RESs. The simulation results indicate the positive effects of DR implementation, BESS deployment, and permission for electricity sales to the upper utility on decreasing RES curtailment and distribution system operation costs. Noticeably, the RES curtailments became zero with the permission of bidirectional power flow. In addition, the adopted BESS model excluding binary variables was also validated. Finally, the effectiveness of the developed simulation analysis platform for day-ahead scheduling was demonstrated

Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values

Supercritical CO2 compressor operation near stall and surge conditions

Instabilities such as stall and surge have been widely studied in open loop gas turbine system for several decades. However, researches on instabilities in a closed loop are limited. Especially, no experimental investigation in a supercritical CO2 compressor has been performed thoroughly before. An integral test loop equipped with a turbo-alternator-compressor using active magnetic bearing was utilized to study the instabilities in a supercritical CO2 compressor. The compressor performance testing was conducted including near stall and surge operating conditions. The mass flow rate of compressor was greatly reduced even beyond surge limit and pressure was measured with high frequency transducer to observe the characteristics. It was observed that as the inertia of the system was changed by altering the compressor inlet conditions, the characteristics of the supercritical CO2 compressor in unstable operating conditions also vary. This can be qualitatively explained by using the theory Greitzer proposed previously

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values

Metastable hexagonal close-packed palladium hydride in liquid cell TEM

Metastable phases-kinetically favoured structures-are ubiquitous in nature1,2. Rather than forming thermodynamically stable ground-state structures, crystals grown from high-energy precursors often initially adopt metastable structures depending on the initial conditions, such as temperature, pressure or crystal size1,3,4. As the crystals grow further, they typically undergo a series of transformations from metastable phases to lower-energy and ultimately energetically stable phases1,3,4. Metastable phases sometimes exhibit superior physicochemical properties and, hence, the discovery and synthesis of new metastable phases are promising avenues for innovations in materials science1,5. However, the search for metastable materials has mainly been heuristic, performed on the basis of experiences, intuition or even speculative predictions, namely 'rules of thumb'. This limitation necessitates the advent of a new paradigm to discover new metastable phases based on rational design. Such a design rule is embodied in the discovery of a metastable hexagonal close-packed (hcp) palladium hydride (PdHx) synthesized in a liquid cell transmission electron microscope. The metastable hcp structure is stabilized through a unique interplay between the precursor concentrations in the solution: a sufficient supply of hydrogen (H) favours the hcp structure on the subnanometre scale, and an insufficient supply of Pd inhibits further growth and subsequent transition towards the thermodynamically stable face-centred cubic structure. These findings provide thermodynamic insights into metastability engineering strategies that can be deployed to discover new metastable phases. © 2022. The Author(s), under exclusive licence to Springer Nature Limited.11Nsciescopu

Photoswitchable Microgels for Dynamic Macrophage Modulation

Dynamic manipulation of supramolecular self-assembled structures is achieved irreversibly or under non-physiological conditions, thereby limiting their biomedical, environmental, and catalysis applicability. In this study, microgels composed of azobenzene derivatives stacked via pi-cation and pi-pi interactions are developed that are electrostatically stabilized with Arg-Gly-Asp (RGD)-bearing anionic polymers. Lateral swelling of RGD-bearing microgels occurs via cis-azobenzene formation mediated by near-infrared-light-upconverted ultraviolet light, which disrupts intermolecular interactions on the visible-light-absorbing upconversion-nanoparticle-coated materials. Real-time imaging and molecular dynamics simulations demonstrate the deswelling of RGD-bearing microgels via visible-light-mediated trans-azobenzene formation. Near-infrared light can induce in situ swelling of RGD-bearing microgels to increase RGD availability and trigger release of loaded interleukin-4, which facilitates the adhesion structure assembly linked with pro-regenerative polarization of host macrophages. In contrast, visible light can induce deswelling of RGD-bearing microgels to decrease RGD availability that suppresses macrophage adhesion that yields pro-inflammatory polarization. These microgels exhibit high stability and non-toxicity. Versatile use of ligands and protein delivery can offer cytocompatible and photoswitchable manipulability of diverse host cells