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.
A demand side management technique is deployed along with battery energy-storage systems (BESS) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study, we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand.
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.
A Demand-side management technique are deployed along with battery energy-storage systems (BESSs) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand
Robust operation of load management control for a building is important to account for the uncertainty in demand as well as any distributed sources connected to the building. This paper discussed an online load management control solution using distributed energy storage (DES) while considering uncertainties in demand as well as DES to reduce peak demand for economic benefit. In recent years’ demand-side management (DSM) solutions using DES such as stationary energy management system (BESS) and plugged-in electric vehicles (PEV) have been popularised. Most of these solutions resort to deterministic load forecast for the day ahead energy scheduling and do not consider the uncertainties in demand and DES making these solutions vulnerable to uncertainties. This study presents an online density demand forecast, k-means clustering of PEV groups and stochastic optimisation for robust operation of BESS and PEV for a building. The proposed method accounts for uncertainties in demand and uncertainties due to mobile energy storage as presented by PEVs. For a case study, we used data obtained from an industrial site in South Korea. The verified results as compared to other methods with a deterministic approach prove the solution is efficient and robust.
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