(569b) Development of an Energy Management System with Energy Storage System and Its Operation Optimization Using Time Series Analysis
AIChE Annual Meeting
Monday, November 17, 2014 - 6:00pm to 8:00pm
Many issues still need to be tackled for the wide introduction of renewable energies. In addition to economic technological maturity, unpredictable nature makes it more challenging to employ renewable energy sources in practice. At the same time, energy demand continuously increases and is subject to vary without considering its generation. The corresponding energy system is under heavy challenges in terms of providing sustainable energy generation methodologies as well as energy demand management.
This paper is concerned with an energy system in terms of both of supply and demand. There are two important features that a new energy system should address. At first, increasing portion of energy supply should be obtained from the renewable energy sources to reduce the negative impact of currently dominating fossil fuels. In evaluating the performance of an energy system, it is thereby important how to hand the resulting irregularity of the renewable energy supply. The estimation of output with regard to external environment should be developed and implemented with the existing energy supply management framework. Secondly, the capricious energy demand should be addressed in the energy system. It is fully motivating to develop a methodology to provide how much energy should be required at the process or a complex of processes in a foreseeable near future. It is of course very difficult to forecast the demand but the previous operating data and the external situation may be of some help.
The corresponding energy system for next generation should incorporate the varying energy supply and demand at the same time. We need a kind of buffer between them and energy storage system can play such a role. To predict the energy generation output of the renewable energies based on the previously obtained data. In this paper time series analysis methods is used to model and forecast the photovoltaic electricity generation and electricity demand. The necessary size of buffering energy storage system is determined by a new framework using Monte Carlo simulation.