Kazuhiko Ogimoto

A Good Fit: Japan¿s Solar Power Program and Prospects for the New Power System

The Japanese earthquake and tsunami of 11 March 2011 spurred numerous discussions and debates regarding the national energy policy of Japan. One of the outcomes of this process was a new feed-in tariff (FIT) program to promote renewable energy, including photovoltaic (PV) systems, that went into effect on 1 July 2012. This new incentive program is expected to completely change the PV market in Japan, which has historically been dominated by residential PV applications. The FIT will drive not only the residential sector but also the nonresidential sector, including the new entrants to the Japanese power generation business: megawatt-scale PV power plants. Accelerating the introduction of renewable energy is important not only for diversifying Japans sources of energy (for energy security) and combating global warming (for the environment) but also for the development of green industries (for the economy). It is essential that Japan create an environment conducive to the expansion of renewable energy in Japan by identifying the appropriate mix of regulatory measures (e.g., FITs), public support, and private-sector voluntary efforts best suited to each energy source. This article describes the current status of the PV market in Japan; the new FIT program and its impacts on the PV market; and further institutional, technical, and R&D challenges for PV dissemination.

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.

Coupled Oscillator Model of the Business Cycle with Fluctuating Goods Markets

The sectoral synchronization observed for the Japanese business cycle in the Indices of Industrial Production data is an example of synchronization. The stability of this synchronization under a shock, e.g., fluctuation of supply or demand, is a matter of interest in physics and economics. We consider an economic system made up of industry sectors and goods markets in order to analyze the sectoral synchronization observed for the Japanese business cycle. A coupled oscillator model that exhibits synchronization is developed based on the Kuramoto model with inertia by adding goods markets, and analytic solutions of the stationary state and the coupling strength are obtained. We simulate the effects on synchronization of a sectoral shock for systems with different price elasticities and the coupling strengths. Synchronization is reproduced as an equilibrium solution in a nearest neighbor graph. Analysis of the order parameters shows that the synchronization is stable for a finite elasticity, whereas the synchronization is broken and the oscillators behave like a giant oscillator with a certain frequency additional to the common frequency for zero elasticity.

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.

Photovoltaic power production forecasts with support vector regression: A study on the forecast horizon

The objective of this study is to verify how different forecast horizons affect the accuracy of a method to forecast photovoltaic power production using support vector regression and numerically predicted weather variables. One year of power production forecasts were done for a 1 MW photovoltaic power plant in Kitakyushu, Japan. Two forecast horizons were evaluated, up to 2 hours ahead and up to 25 hours ahead. The results showed a variation of the accuracy of the forecasts according to the forecast horizon. The root mean square error for 1 year of up-to-2-hours-ahead forecasts was 0.104 MWh; whereas for the up-to-25-hours-ahead it was 0.118 MWh. The mean absolute error was 0.065 MWh for the up-to-2-hours-ahead forecasts and 0.076 MWh for the up-to-25-hours-ahead forecasts. In percentage terms, the root mean square error and the mean absolute error increased 13% and 17%, respectively, with the increase of the forecast horizon.

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.

A national project on Optimal Control and demonstration of the Japanese smart grid for massive integration of photovoltaic systems

This paper describes one of Japanese smart grid related activities, “Demonstration Projects for Next Generation Optimum Control of Power Transmission and Distribution Network” which deals with issues associated with mass penetration of photovoltaic power generation systems. Research subjects covered by this project and some results obtained so far are presented.

Estimations of cost and CO 2 emissions for smart grids using a linear programing approach

A smart grid is an electricity network that uses digital technology to monitor and manage the transport of electricity from all generation sources to meet the varying electricity demands of end users. In this paper, the smart grid is defined as the electric power system consistent with the BASE or BLUE scenarios in the Energy Technology Perspectives 2010 with the large scale renewable integration and the capability of demand response. Quantitative estimations of the cost and the CO2 emissions are made using a linear programing approach for the BASE and BLUE smart grids in the US. Among the BASE smart grids, a smart grid where a high penetration of renewables is considered is more expensive than the BASE scenario. The increased cost was due to expensive renewables. Another smart grid where both high renewables and load shifting, were applied demonstrated the least CO2 emissions. The trend in increasing CO2 emissions continues 2050. Among the BLUE smart grids, both cost and CO2 emissions were decreased when load shifting was applied. Temporal change of CO2 emissions clearly shows a decreasing trend. Then, the capacity of the electric storage required for the integration of renewables at 2050 was estimated to be 9.0GW. Batteries used in electric vehicles will be used for the electric storage at low cost. Finally the cost saving for the load shifting was estimated to be 1.64 × 1011 USD by a break-even point analysis. The cost saving is equivalent to the yearly saving of 3.64USD/household/year for forty five years. If the saving of 3.64USD/household/year is not enough for utilities to cover the cost increased due to the load shifting equipments, the utilities and customers have to share the extra cost.