Takashi Ikegami

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%.

Optimum operation scheduling model of domestic electric appliances for balancing power supply and demand

High penetration of variable renewable generations such as Photovoltaic (PV) systems will cause the issue of supply-demand imbalance in a whole power system. Activation of residential power usage, storage and generation by sophisticated scheduling and control using the Home Energy Management System (HEMS) will be needed to balance power supply and demand in the near future. In order to evaluate the applicability of the HEMS as a distributed controller for local and system-wide supply and demand balances, we developed an optimum operation scheduling model of domestic electric appliances using mixed integer linear programming. Applying this model to one house with dynamic electricity prices reflecting the power balance of the total power system, it was found that the adequate changes in electricity prices bring about the shift of residential power usages to control the amount of the reverse power flow due to excess PV generation.

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.

Balancing power supply-demand by controlled charging of numerous electric vehicles

High penetration of variable renewable energy generation such as photovoltaics and wind power generation will cause a supply-demand imbalance in the entire system. The mass deployment of electric vehicles (EVs) and plug-in hybrid vehicles (PHVs) will also cause significant changes in electricity demand. Therefore, controlling and managing the charging time of EVs/PHVs are imperative for power system operation. We assumed trip patterns of 10 million EVs in the Tokyo power system and analyzed the power system loads, including the charging load of EVs, in some scenarios. We verified quantitatively that charging-time controls are absolutely necessary because the fuel costs for generating power by thermal power plants increase without charging-time controls. Further, we found that load leveling is more effective for multi-car charging management than for single-car charging management.

Short-term forecasting of residential building load for distributed energy management

It is expected that energy management systems (EMS) on the demand side can be used as a method for enhancing the capability of balancing supply and demand of a power system under the anticipated increase of renewable energy generation such as photovoltaics (PV). Energy demand and solar radiation must be predicted in order to realize the optimal operation scheduling of demand side appliances by EMS, including heat pump water heaters, PV systems, and solar powered water heaters. This paper presents a day-ahead forecasting method for electricity consumption in a house to contribute to energy management. Ten forecasting methods are examined using real survey data from 35 households over a year in order to verify forecast accuracy. A daily battery operation model is also developed to evaluate the effect of load forecasts.

Geographical smoothing effects on wind power output variation in Japan

There is a growing concern that the supply and demand operations of electric power systems will become more difficult with a large number of renewable energy sources whose outputs depend on the weather variations. To investigate the countermeasures that are required to stabilize the outputs of the power systems, it is important to quantitatively evaluate the wind power output fluctuations and variations corresponding to the supply-demand balancing controls, such as turbine governor control, load frequency control, and economic-load dispatching control (EDC). With an increasing number of wind power generators being installed, these fluctuations and variations with respect to the rated output capacity will decrease because of the geographical smoothing effects, i.e., the output fluctuations and variations of multiple wind turbines will mutually cancel one another. In this study, we quantitatively evaluate the geographical smoothing effects using the actual high time-resolution wind power output data of several wind farms that is recorded in the supervisory control and data acquisition systems of the Tohoku and Kyushu power systems in Japan. Further, we evaluate the smoothing effects pertaining to the introduced wind power using various approximation curves, and it is observed that the exponential parts of the approximation curves depend on the region and cycles. Using these exponential parts, we observe that perfect smoothing effects were obtained during the shorter periods of cycles. The smoothing effects of the fluctuations in the EDC domain are observed to be small, especially in the Tohoku region.