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.

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.

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.

To advance carbon neutrality policies, many countries are increasingly integrating Renewable Energy Sources (RESs) into their energy mix. However, for harnessing natural energy in power generation, RESs present challenges in output control, potentially leading to power imbalances. Such imbalances, when coupled with an excessive reliance on unpredictable RES, may necessitate the curtailment of grid-integrated renewable generation, halting production despite available capacity. This paper proposes strategies to mitigate the economic and operational efficiency impacts of increased RESs on the power grid. It focuses on the optimal operation of Battery Energy Storage Systems (BESSs) and Power to Gas (P2G) technology, which flexibly converts surplus electricity into hydrogen. By employing Mixed Integer Linear Programming (MILP) for optimization and Stochastic Programming (SP) for the management of the uncertainty of renewable energy output, this paper evaluates the anticipated benefits of BESS and P2G applications.

In South Korea, Renewable Energy Sources (RES) have been increasing with the application of energy policies, such as Feed in Tariff (FIT) and the Renewable Portfolio Standard (RPS). However, a rapid increase in RES supply leads to an uncertain power supply due to the intermittent output of RES. A representative example is the curtailment of Wind Turbines (WT), which frequently occurs in Jeju Island, South Korea. The proportion of RES power on Jeju Island is 67%, and there are cases where WT is curtailed among the operable sections to maintain the balance of power supply and demand. This paper applies Power-to-Gas (P2G) technology to hydrogenate, store, and utilize unused power to solve this problem. In this paper, Aewol-eup in Jeju Island is selected as a target site for case study. An Integrated Energy System (IES) for various energy operations is designed to control RES output. This paper proposes the optimal facility configuration and finally drives the optimal design and operation solution of IES by analyzing the objective functions and focusing on the Independent Power Producer (IPP) perspective.

The purpose of this study is to optimize the operation of wind power generation-based standalone microgrid linking P2G technology. Renewable energy sources, such as wind turbine, photovoltaic, etc., use natural energy as a power source, so the system operator could not control the power of the renewable energy sources. Failure may occur and damage such as power failure may occur if the facilities of the RES that cannot be controlled output increase and the balance of power supply and demand in the power system is not matched. In this study, insufficient power is supplemented by power supply using diesel generator, and excess power is supplemented by Battery Energy Storage System (BESS) and Power-to-Gas (P2G) technologies. In particular, P2G technology using hydrogen energy is mainly dealt with, and multi-objective optimization programming that satisfies both objective functions is interpreted based on pareto-front optimization by deriving the power limit of wind power generators and system costs incurred when building and operating microgrids.