Naoaki Yamanaka

QoS aware energy optimal network topology design and dynamic link power management

To construct energy optimal Internet, a new traffic engineering concept aggregating traffic into specific links and dynamically powering off interfaces of routers/switches should be introduced. This paper proposes a network topology design algorithm and examines link power management.

MiDORi: Energy efficient network based on optimizing network design tool, remote protocol and new layer-2 switch

A network using minimum set of nodes and links that all communication traffic can be carried while meeting the QoS requirements named MiDORi are proposed. The proposed algorithm together can realize energy efficient IP/Ethernet.

Demonstration of the self organized dynamic link power management by “MiDORi” energy optimal network topology design engine

This paper reports the demonstration results of the dynamic link power management by energy optimal network topology design with prototype Gigabit layer-2 switches. The result shows dynamic link power management is feasible.

Distributed demand scheduling method to reduce energy cost in smart grid

Smart grid has attracted attentions and expresses a view for the future power systems in the world. Demand Side Management(DSM) is a significant method in smart grid that helps the energy providers to shift behind the peak load. Some methods to reduce peak load have been studied in dynamic pricing. One of the purposes is to delay the demand to periods of low electricity price. The conventional scheduling method makes load curve that is inversely proportional to electricity prices as an objective load curve, and this strategy delay the expected demand curve as close as to the objective load curve. There is a problem, however, the scope of controlled object is limited and the whole load situation is not considered. This paper presents a method to smooth the demand situation of each house to reduce electricity prices by a Genetic Algorithm, in consideration of the amount of the whole demand and coordination between each group. The proposed method is able to give a higher value of objective to the group which has more shiftable demand, by adjusting the objective curve considering the delayable time. This feature makes the load as close as the object and reduces electricity price. Simulation results show that the proposed algorithm achieve the reduction of the peak load and the utility bill in smart grid.

Highly-accurate short-term forecasting photovoltaic output power architecture without meteorological observations in smart grid

We propose a forecasting architecture of near future photovoltaic output power based on the multipoint output power data via smart meter. The conventional forecasting methods are based on the analysis of meteorological observation data, and need the implementation of dedicated meters and the connection to them. Moreover, highly-accurate forecasting(in second-scale, or meter-scale) is difficult in the conventional methods. Our proposed method is based on not meteorological observation data but the actual measured output power data by using the solar panels connected with a smart meter as sensing units. A forecasting calculation server interpolate spatially the actual measured data collected from multipoint, and forecasts near future output power in each point using optical flow estimation. Virtual sampling technique involves the forecast performance when the sampling point is sparse. We show the forecasting method achieves high accuracy of less than 5% error rate by the computer simulation.

Distributed control method for Micro Combined Heat and Power system on logical token-ring model in real-time pricing

The interest on distributed generation has been increasing, especially due to technical development on generation systems that meet environmental and energy policy concerns. One of the most important distributed energy technologies is Micro Combined Heat and Power (Micro-CHP). It can reduce power loss and energy cost, and enhance service reliability in distribution systems. The control-by-price concept fits well with controlling distributed generation and demand, however, It is difficult to control all Micro-CHP as expected using only real-time-pricing. Centralized control methods, which communicate bidirectionally, timely, and heavily between the operator and each house, have the problem from the point of view of the traffic, however, we selected a decentralized control model which communicate between each house and the operator manages only total energy information. This work proposes a new decentralized control method to operate multi Micro-CHP totally by taking token-ring with houses. Multi houses make a group, and coordinate their total generation by themselves, then solve imbalance of supply and demand without the operators indication to each house. In addition, to sustain fairness in the system, priority based scheduling algorithms which turn rights of generation as token are proposed. By computer simulation, it is shown that the proposed method can reduce over 90 % the disposal energy and 40 % the total energy costs at most while sustaining fairness. Thus, the proposed method use effectively the generation of Micro-CUP according to the spread of distributed generation.

EDA-Based Charging Algorithm for Plug-In Hybrid Electric Vehicle to Shift the Peak of Power Supply

This paper proposes a charging algorithm which is based on the Estimation of Distribution Algorithm (EDA) for Plug in Hybrid Electric Vehicle (PHEV). The proposed algorithm shifts the peak of power supply, satisfies the requested State of Charge (SoC [%]), and minimizes the charging cost. The proposed algorithm uses flexible weight for charging and upper charging limit to each PHEV to minimize the charge amount in peak time. The simulation result shows that the proposed algorithm shifts the peak of power supply in commute time zones, satisfies SoC of 93% PHEVs, and reduces charging cost by 31% compared with conventional EDA algorithm.