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Unit Commitment for Renewable Energy Sources
Unit Commitment for Renewable Energy Sources
The amount of renewable energy sources connected to the grid is increasing due to the global trend of decarbonization and the FIT system. On the other hand, because of the uncertainty in output prediction and the short-period variability of renewable energy sources, the stability of the grid is threatened. Therefore, the output of renewable energy sometimes has been curtailed in the Kyushu area. In this study, with multiple approaches such as the use of tie lines and energy storage facilities, a unit commitment model that makes effective use of renewable energy sources is developed.
The amount of renewable energy sources connected to the grid is increasing due to the global trend of decarbonization and the FIT system. On the other hand, because of the uncertainty in output prediction and the short-period variability of renewable energy sources, the stability of the grid is threatened. Therefore, the output of renewable energy sometimes has been curtailed in the Kyushu area. In this study, with multiple approaches such as the use of tie lines and energy storage facilities, a unit commitment model that makes effective use of renewable energy sources is developed.
Utilizing EVs in Load Frequency Control
Utilizing EVs in Load Frequency Control
The power system requires more regulation ability when large amount of renewable energy with unpredictable generation is injected to the grid. Demand side response like electric vehicles(EVs) are potential regulation resource that could join the ancillary market to help to maintain the system stability. In this research, we examine the possibility of EVs joining load frequency control(LFC). One main problem is that the communication delay of control signals from the local system operator to individual EVs could deteriorate the performance. The current objective of this research is to compensate for the delay effect though a properly designed controller on the EV side. Field experiment like Hardware-in-the-loop simulation is expected to be done in the future.
The power system requires more regulation ability when large amount of renewable energy with unpredictable generation is injected to the grid. Demand side response like electric vehicles(EVs) are potential regulation resource that could join the ancillary market to help to maintain the system stability. In this research, we examine the possibility of EVs joining load frequency control(LFC). One main problem is that the communication delay of control signals from the local system operator to individual EVs could deteriorate the performance. The current objective of this research is to compensate for the delay effect though a properly designed controller on the EV side. Field experiment like Hardware-in-the-loop simulation is expected to be done in the future.
Risk Management of Local Power Retailer with Hydrogen Production
Risk Management of Local Power Retailer with Hydrogen Production
Since the beginning of the complete liberalization of the electricity market, a lot of local Power Producer and Supplier (PPS) have been established that contribute to the local production and consumption of energy and the revitalization of the local economy. However, PPS has financial problems due to the uncertainty of JEPX price and output of power generation resources. In this research, a business model using hydrogen is proposed, and the management risk is evaluated quantitatively by using financial engineering methods such as portfolio theory. And indicating the optimal management method of PPS is aimed.
Since the beginning of the complete liberalization of the electricity market, a lot of local Power Producer and Supplier (PPS) have been established that contribute to the local production and consumption of energy and the revitalization of the local economy. However, PPS has financial problems due to the uncertainty of JEPX price and output of power generation resources. In this research, a business model using hydrogen is proposed, and the management risk is evaluated quantitatively by using financial engineering methods such as portfolio theory. And indicating the optimal management method of PPS is aimed.
Previous Research Themes
Previous Research Themes
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