Enhanced Automated Targeting Model for Multi-Period Energy Planning
Kong, Karen Gah Hie
Lo, Shirleen Lee Yuen
How, Bing Shen
Leong, Wei Dong
Teng, Sin Yong
Ng, Wendy P.Q.
Sunarso, Jaka
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Kong K.G.H., Lo S.L.Y., How B.S., Leong W.D., Teng S.Y., Ng W.P., Sunarso J., 2020, Enhanced Automated Targeting Model for Multi-Period Energy Planning, Chemical Engineering Transactions, 81, 607-612.
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The shortage of non-renewable power supplies and critical environmental issues such as climate change, urban sprawl, ozone layer depletion and excessive carbon emission are the main driving forces that urged many countries and non-profit organisations fully committed to seeking more sustainable energy sources and energy planning. Various Process Integration techniques have been developed, extended and utilized in the energy planning sector. Based on the literature review, the use of time-sliced based models in energy integration is still limited. This paper aims to develop time-sliced models that can be applied into an energy integration model that promises higher energy efficiency in power generation energy planning. To accomplish this, a two-stage framework involving (i) targeting and (ii) scheduling is proposed. The targeting step is to determine the minimum amount of renewable energy sources needed to meet the carbon emission limit whereas the scheduling step is to discover the optimal scheduling of introduced renewable energy sources to mitigate the total electricity bill. It is proposed that with the aid of adequate planning, the economic benefit of utilising renewable energy can be realized. A case study in Malaysia that incorporates an actual billing system is used to demonstrate the effectiveness of the model in reducing both carbon emission and energy cost simultaneously. With the use of the proposed framework and developed model, 46.9 % of electricity bill can be reduced while emission is reduced by 40 % compared to the initial emission.
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