Elsevier

Energy

Volume 32, Issue 9, September 2007, Pages 1617-1633
Energy

Evaluation of city-scale impact of residential energy conservation measures using the detailed end-use simulation model

https://doi.org/10.1016/j.energy.2007.01.007Get rights and content

Abstract

Energy conservation policies for the residential sector are evaluated by a model that simulates city-scale energy consumption in the residential sector by considering the diversity of household and building types. In this model, all the households in the city are classified into 380 categories based on the household and building type. The energy consumption for each household category is simulated by the dynamic energy simulation model, which includes an energy use schedule model and a heating and cooling load calculation model. Since the energy usage of each appliance is simulated for every 5 min according to the occupants’ energy usage activity, this model can evaluate not only the energy conservation measures by improving the buildings and appliances but also the measures that involve changing the occupants’ activities. The accuracy of the model is verified by comparing its results with the statistical and the measured data on Osaka City, Japan. Various types of energy conservation measures planned by the Japanese government for the residential sector are simulated and their effects on Osaka City are evaluated quantitatively. The future effects of these combined measures on the energy consumption are also predicted.

Introduction

In Japan, energy consumption in the residential sector has been increasing continuously due to improvements in the standard of living, such as enlargement of houses, popular use of various types of home electric appliances, and an increase in the number of small families. In the last 25 years, the energy consumption of the residential sector has doubled while the population has increased by only 10% [1].

To achieve the 6% greenhouse gas reduction commitment in keeping with the Kyoto Protocol, various kinds of measures were proposed for the residential sector in Japan. The “New Climate Change Policy Program,” which was adopted in March 2002, aims to maintain the CO2 emission from energy usage at its levels in 1990. However, CO2 emission from energy use in the residential sector had increased from its level in 1990 by 28.8% in 2002 due to an increase in the number of household appliances. According to this increase, the “Kyoto Protocol Target Achievement Plan,” which was adopted in April 2005, aims to reduce CO2 emission from the residential sector such that it is 6% greater than its level in 1990 by strengthening the energy efficiency measures for buildings and appliances.

As a part of these programs, the revised version of the Law Concerning Rational Use of Energy established one of the highest energy efficiency standards—commonly known as the “Top-runner Standard”—in the world for home electric appliances. According to this standard, an appliance manufacturer's average energy efficiency in 2004 must be higher than that of the most efficient model in 1999. This standard was revised in 2005 to extend it to more types of appliances.

Besides the top-runner standard, the New Climate Change Policy Program specifies various kinds of energy conservation measures such as increase in energy efficient residential buildings, reduction in standby power, promotion of high-efficiency water heaters. The program also specifies the change in occupants’ behavior such as “easing set room air temperature,” “family members staying together in the living room and not in their individual rooms,” and “reducing the number of hours spent watching TV.”

The effects of these measures are interactive with other measures. For example, energy efficient appliances and change in the occupants’ schedule affects the cooling and heating load. Sezgan and Koomey [2] estimated the interaction between the lighting and space conditioning energy use in commercial buildings. Therefore, these measures should be evaluated by a model that can treat the combination of these measures simultaneously.

Generally, it is very difficult to quantitatively estimate the city or national scale effects of these measures. The quantitative evaluation of energy conservation measures has thus far been based on the simulation results for “a standard household,” which implies a family comprising two adults and two children. However, the energy consumption of each household differs considerably depending on the household type (number and age of members), building type, the number and efficiency of appliances, the occupants’ activity, and other factors. Ultimately, to quantify the city-scale effect of the various energy conservation measures, which include the dissemination of energy efficient appliances and buildings and change in the occupants’ behavior correctly, a “virtual city model” must be developed. This model should be capable of simultaneously simulating the operation of all appliances and the occupants’ behavior in all households within the objective region without using “unit energy consumption per household/person/floor area.”

Clarke et al. [3] applied a building simulation program for estimating the effect of the improvement in a thermodynamic class such as window size and insulation level by considering the present distribution of house types and thermodynamic classes in Scotland. Jones et al. [4] developed a model that estimates the residential energy used in a city by considering the distribution of the building energy used based on Geographical Information System techniques. Brownsword et al. [5] developed the urban energy model which simulates spatial and diurnal variations of energy demand based on diurnal demand profile of each consumer type. However, these models could not consider the energy use by each appliance. Michalik et al. [6] developed a structure model of electricity demand in the residential sector of a region based on a bottom-up approach that sums up each appliance's operation schedule for improving the electricity load curve by demand side management. However, this method does not include the heat load calculation and the fuel consumption.

The authors [7] have developed a bottom-up simulation model that simulates the city-scale energy consumption in the residential sector by considering the diversity of household and building types. In this model, the energy consumption for each household category was simulated by the appliance energy use model, hot water supply model, and heating and cooling model. In the appliance energy use model, the energy use of each appliance was simulated individually based on the schedule data of the occupants’ behavior. In the heating and cooling model, the cooling and heating load was simulated from the building data and weather data. The internal heat gain, which was calculated by the appliance energy use model, and the occupants’ behavior schedule were also used in this model. Since rooms in Japan are commonly equipped with room air conditioners and heaters, which are intermittently used for air-conditioning, consideration of the occupants’ behavior schedule is necessary to correctly estimate the energy consumption for heating, cooling, and lighting as well as the energy consumption of appliances such as televisions. In Japan, the time allocation of living activities is surveyed every 5 years by Broadcasting Culture Research Institute [8]. These results can be used for modeling the occupants’ energy use schedule [9].

In this paper, our previous model was improved in terms of the heat load calculation, simulation of the occupants’ behavior schedule, and so on. The new model has been applied to Osaka City (population: 2598 thousand, households: 1044 thousand). The present amount of energy consumption in the residential sector is estimated and compared with the statistical data. In the final part of this paper, the energy conservation effects of the various kinds of measures are evaluated quantitatively.

Section snippets

Structure of the simulation model

Fig. 1 shows the structure of the simulation model. In this simulation, the annual energy consumption of one household is calculated iteratively for 19 household categories and 20 building categories—10 categories for detached houses and 10 categories for apartment houses are set depending on the floor area. In addition, five types of building insulation levels are assumed. Each occupant's time allocation for living activities, amount and temperature of hot water supply, weather data, and

Simulation results for each household

In this paper, energy consumption is indicated as the “primary energy consumption,” and electricity consumption is calculated by the following relation: 1 kWh of electricity=9830 kJ of primary energy consumption.

Fig. 5 shows the distribution of the simulated annual primary energy consumption of one household in a detached house and an apartment house. These figures show that the total energy consumption depends more on the number of family members than the total floor area. The influence of the

Evaluation of various energy-saving measures

One significant advantage of this model is its simulation of the heating and cooling loads precisely by coupling the dynamic heat load simulation and energy use schedule model. Further, it enables to consider the change in the energy consumption due to heat insulation of buildings, climate conditions, and schedules of living activities. Accordingly, the effect of a heat insulation standard, an energy efficiency standard for room air conditioners and an introduction of daylight saving time are

Conclusion

In this paper, a new “virtual city” end-use model, which simultaneously simulates the operation of all appliances and occupants’ behavior in all households in the city, is developed. This model can quantitatively estimate the city-scale effect of various types of energy conservation measures such as energy efficient appliances, insulation of buildings, and change in occupants’ behavior.

Using this model, various types of energy conservation measures in the residential sector proposed by the

Acknowledgment

This work is supported by Grants-in-Aid for Scientific Research, Japan Society for the Promotion of Science, Nos. 15360310 and 18360273.

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