Search

This paper developed an agent-based model (ABM) to explore the energy saving potentials (ESPs) of various types of appliances in offices under different pricing mechanisms. The model included four types of commonly used appliances in office buildings: an air conditioner (AC), computers, lights and a basic load. The total ESPs of the entire office are 6.7% and 17.4% on the second and the third price tier of the tiered pricing mechanism (TEP), while the ESPs are 11.8% and 14.2% under the peak-valley pricing (PVP) and critical peak pricing (CPP), respectively. Within different types of appliances, AC consumes the largest amount of electricity, over 50%, while the ESPs of the AC under different pricing mechanisms are only 6.9–12.1%. In contrast, the lights have the biggest ESP, i.e. 14.1–53.4%, under various pricing levels. Both the pricing mechanisms of PVP and CPP only have the effect of peak clipping and do not have a significant effect of valley filling, since there is no people working in the office during the valley price period. The maximum ESP, which is based on people’s maximum-saving behavior, is much larger than the ESPs on the basis of people’s ordinary consumption patterns. This implies the importance of improving people’s awareness of energy saving and refining their behaviors. Lastly, the model developed in this study provides a generic platform for simulating many types of energy systems and is very effective for handling the complicated relations between different types of technology and the way how they are used and interacted with each other. ABMs have very good adaptability and capacity in simulating energy systems. [ABSTRACT FROM AUTHOR]

An agent-based model is developed to estimate the potential of energy savings of an office room under different pricing mechanisms. For this aim, an energy balance model was developed to simulate the operation of air conditioner operation, the heat flux of different behavior and the electricity consumption of different types of appliances according different energy consumption patterns was investigated in detail. The total electricity consumption reduced 3.9%-17.1% under different pricing mechanisms. Light's energy saving potential is the biggest among the electric appliances. The maximum-saving behavior is investigated and the total energy saving potential of it is 26.91%. [ABSTRACT FROM AUTHOR]

Conventional approaches for modelling energy consumption for data center are limited to deal with the randomness, emergence and interdependence in energy systems. To fill the gap, an agent-based method was proposed and implemented to simulate the electricity consumption of a data center and to explore the potential of energy saving. The study found that a large amount of energy can be saved by dynamically operating the cooling load according to the practical load of the servers, namely 13% of the total energy consumption can be saved in the dynamic energy saving case (DESC) and 17% in the enhanced dynamic energy saving case (EDESC). The power usage effectiveness (PUE) of the data center can decrease from 1.71 in the original situation to 1.49 in the DESC and 1.42 in the EDESC. By contrast, the frequency of data backup has a heavy impact on the electricity consumption of the data center, while the frequency of computing tasks do not. [ABSTRACT FROM AUTHOR]

In this paper, we developed an agent-based model to simulate the electricity consumption in an office building under a tiered pricing mechanism. In the model, electricity users’ behavior is significantly influenced by the electricity price. The price and the range of each tier have great impacts on daily and total consumption, these parameters are discussed by sensitivity analysis. The result was compared with the consumption under single pricing. We found the daily consumption reduced more than 20% under the third tier price, the price change's influence has huge difference among different electric appliances. [ABSTRACT FROM AUTHOR]