The increasing penetration of renewable resources causes some challenges like the electric power demand prediction uncertainty and energy surplus. Energy storage systems (ESS) are promising solutions for these challenges. However, considering the marginal capacity of ESSs according to the installation area and the economic portion of ESSs according to the installation capacity, the use of battery ESSs to reduce surplus energy is not efficient and has practical limitations. To efficiently resolve the challenges, a multi-energy system (MES) that is capable of operating different energy sources, such as natural gas storage (NGS), thermal energy storage (TES), ice energy storage (IES), and hydrogen energy storage (HES) has been proposed. The centerpiece of converting and managing multiple energy sources associated with the MES is the energy hub (EH). In this paper, we reviewed and compared the performance of existing ESSs and the MES, and the results have demonstrated the superiority of the MES. In addition, EHs that include power-to-gas, combined heat power, and combined cooling heat power, have been examined based on their structural characteristics. A review of the methods and the primary purpose of MES is also highlighted in this paper.

Various polybenzimidazole (PBI)-based ion-exchange films were prepared and thoroughly characterized by Fourier transform infrared (FT-IR) spectroscopy, proton conductivity, and water uptake for possible use as fuel cell membranes. Upon the increase in the flexibility of the PBI-based polymer films (e.g., poly(oxyphenylene benzimidazole) (OPBI) and sulfonated OPBI (s-OPBI)), the membranes exhibited slightly improved proton conductivity, but significantly increased dimensional changes. To reduce the dimensional changes (i.e., increase the stability), the cross-linking of the polymer films (e.g., cross-linked OPBI (c-OPBI) and sulfonated c-OPBI (sc-OPBI)) was accomplished using phosphoric acid. Interestingly, the sc-OPBI membrane possessed a greatly increased proton conductivity (0.082 S/cm), which is comparable to that of the commercially available Nafion membrane (0.09 S/cm), while still maintaining slightly better properties regarding the dimensional change and water uptake than those of the Nafion membrane.

As natural disasters and abnormal weather phenomena continue to cause damage to power systems, resilience has become a critical characteristic for future power grids. While microgrids (MGs) have been studied as a potential source of resilience, previous research has mainly focused on operational strategies to improve the resilience of existing MGs. This study proposes a planning approach for a gas-electric-based integrated multi-energy system that includes a microgrid to enhance the power grid's resilience against serious failures. The approach includes an operational process to achieve a specific resiliency level at the lower level, and a bi-level optimization method that considers cost effects at the upper level. The proposed method uses mixed-integer linear programming (MILP) to establish the operational process based on the required resilience level and determine the optimal location for each asset in the microgrid. The effectiveness of the proposed method is evaluated using the IEEE 33 Bus test system in various severe failure scenarios. The results demonstrate that the proposed planning approach can effectively enhance the resilience of power grids through the integration of microgrids, and the bi-level optimization method can provide an optimal solution while considering the cost-effectiveness of the microgrid.

The microgrid is a power distribution system that supplies power from distributed generation to end-users. Demonstration projects and R&D regarding microgrids are currently in development in several advanced countries. In South Korea, renewable energy-based microgrid demonstration projects are carried out mainly as island or university campus grids. These R&D efforts aim to popularize microgrid systems in South Korea while considering the limited land availability, which impedes the widespread distribution of photovoltaic systems and the microgrid market’s growth. This study presents a floating photovoltaic system configured as an island microgrid combined with a hybrid power system. The floating photovoltaic system is configured on an idle water body integrated with an existing pumped hydroelectric system. The integration of a current pumped hydroelectric system minimizes a battery energy storage requirement, which compensates for the renewable energy sources’ intermittent power output. We evaluate the optimal power flow of the setup using a reliability index to ensure a stable power supply within the standalone microgrid and maximize the supply power range according to the demand response.

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to generalise well. Most learning algorithms produce a better forecast for dominant patterns in the case of weekday consumption and otherwise for less dominant patterns in weekend and holiday consumption. In view of this, there is the need to cluster the load patterns, so learning algorithms can focus on the patterns independently to produce forecasts with better accuracy for all cases. However, clustering time-series data breaks the time-series dependency, making model training difficult. This paper presents a novel sequence-to-sequence cluster framework to reform time-series dependency after clustering; this enables independent clusters to be modelled using Convolutional Neural Network-Gated Recurrent Unit, which learns spatiotemporal features for future forecasts. A real-world dataset by the Korea Power Exchange composed of nationwide consumption is used for case studies and experiments. Experimental results verify that the proposed study effectively improves the accuracy of electric load forecasting by about 50%, with a WAPE of 0.67%. The proposed method also speeds up the training process of the forecast algorithm by about 35%, given that only a subset of the dataset is trained due to clustering. Korea Water Resources Corporation has implemented the proposed method for load forecasting and system marginal price estimation.