Kazuto Kataoka

A novel charging-time control method for numerous EVs based on a period weighted prescheduling for power supply and demand balancing

To establish a sustainable energy supply system, renewable energy sources and low-carbon vehicles will have to become more widespread. However, it is often pointed out that the dissemination of these technologies will cause difficulties in balancing supply and demand in a power system, due to the fluctuation in the amounts of renewable energy generated and the fluctuation in the power demanded for numerous electric vehicles (EVs). The numerous EVs charging control seems to be difficult due to the difficulties in predicting EV trip behaviors, which vary depending on individual EV users. However, if we can control the total demand of numerous EVs, we can not only level their total load shape but also improve the supply-demand balancing capability of a power system to create new ancillary service businesses in the power market. This paper proposes a novel centralized EV-charging-control method to modify the total demand of EV charging by scheduling EV charging times. The proposed method is expected to be a powerful tool for a power aggregator (PAG), which will supply EV charging services to EV users and load leveling services to transmission system operators (TSOs) without inconveniencing EV users. The simulation showed that under the assumed EV trip patterns, the total charging demand of numerous EVs was successfully shaped so that the differences between watt-hours of the requirement and those of the controlled results were less than 4%.

A unit commitment model with demand response for the integration of renewable energies

The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms. First we analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increased with the forecast error. Then the effect of a sudden decrease in wind power is analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total operation cost significantly. Finally, the effects of the demand response in the case of a sudden decrease in wind power are analyzed. We confirm that the number of operating thermal power plants is reduced by the demand response. A power utility has to continue to use thermal power plants for ensuring the supply-demand balance; some of these plants can be decommissioned after installing a large number of wind farms or PV systems, if the demand response is applied with an appropriate price structure.