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.
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.
Reliability is an important index which determines the power service and quality provided to customers. As the demand increases continuously and the system changes in accordance with the environmental regulation, the reliability assessment in the distribution system becomes crucial. In this paper, we propose methods for improving the reliability of the distribution system using electric vehicles (EVs) in the system. In this paper, EVs are used as power supplying devices, such as a transportable energy storage system (ESS) which supplies power when fault occurs in the system, and by using a time–space network (TSN) in particular, EV capacity in accordance with the load arrival time was calculated. Unlike other existing reliability assessments, we did not use the average load of customers. Instead, by taking into account the load pattern by times, we considered the priority for load supply in accordance with the failure scenarios and failure times. Based on the priority calculated for each time of failure and failure scenario, plans for EV operation to minimize expected customer interruption cost (ECOST), the reliability index in the distribution system, were established. Finally, a case study was performed using the IEEE RBTS (Roy Billinton Test System) 2 Bus and the performance of the model proposed in this paper was verified based on the result.
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.INDEX TERMS Convolutional neural network-gated recurrent unit (CNN-GRU), feature engineering, kmeans clustering, LightGBM classifier, sequence-to-sequence forecast, short-term load forecast (STLF).
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