Naoto Tagashira

The Modifiable Areal Unit Problem, in a Regression Model Whose Independent Variable Is a Distance from a Predetermined Point

This paper deals with the modifiable areal unit problem in the context of a regression model where a dependent variable is an attribute value (say, income) of an atomic data unit (say, a household) and an independent variable is a distance from a predetermined point (say, a central business district) to the atomic data unit (a disaggregated model). We apply this disaggregated model to spatially aggregated data in which the dependent variable is the average income over a spatial unit and the independent variable is the average distance from each household in a spatial unit to the predetermined point (an aggregated model). First, estimating the slope coefficient by the least squares method, we prove that the variance of the estimator for the slope coefficient in the aggregated model is larger than that in the disaggregated model. Second, focusing on variations in the variance of the estimator for the slope coefficient in the aggregated model with respect to the number of zones, we obtain the number of zones in which the variance is close to that in the disaggregated model. Third, we obtain the zoning system that has the minimum variance for a fixed number of zones. We also calculate the maximum variance in order to examine the range of the variance.

EV charging schedule for load leveling by non-linearly-distributed start time

Large-scale deployment of electric vehicles (EVs) adds load to the current power system. Without control of the temporal dispersion of EV charging, a new rapid peak load may occur and cause severe problems in the power system. Therefore, we propose two algorithms for load leveling by decentralized autonomous control. The first algorithm uses an off-peak rate period, and the second changes the charging start time according to the charging duration. We evaluated the second algorithm in two cases: a linear case, where the start time varies linearly with the charging duration, and a quadratic case, where the start time varies non-linearly, that is, quadratically with the charging duration. In the linear case, a gradually sloped peak occurs in the morning. On the other hand, in the quadratic case, the charging hours are dispersed appropriately, and the daily load curve is almost flat in the night.