TY - GEN
T1 - Optimizing power, heating, and cooling capacity on a decision-guided energy investment framework
AU - Ngan, Chun Kit
AU - Brodsky, Alexander
AU - Egge, Nathan
AU - Backus, Erik
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - We propose a Decision-Guided Energy Investment (DGEI) Framework to optimize power, heating, and cooling capacity. The DGEI framework is designed to support energy managers to (1) use the analytical and graphical methodology to determine the best investment option that satisfies the designed evaluation parameters, such as return on investment (ROI) and greenhouse gas (GHG) emissions; (2) develop a DGEI optimization model to solve energy investment problems that the operating expenses are minimal in each considered investment option; (3) implement the DGEI optimization model using the IBM Optimization Programming Language (OPL) with historical and projected energy demand data, i.e., electricity, heating, and cooling, to solve energy investment optimization problems; and (4) conduct an experimental case study for a university campus microgrid and utilize the DGEI optimization model and its OPL implementations, as well as the analytical and graphical methodology to make an investment decision and to measure tradeoffs among cost savings, investment costs, maintenance expenditures, replacement charges, operating expenses, GHG emissions, and ROI for all the considered options.
AB - We propose a Decision-Guided Energy Investment (DGEI) Framework to optimize power, heating, and cooling capacity. The DGEI framework is designed to support energy managers to (1) use the analytical and graphical methodology to determine the best investment option that satisfies the designed evaluation parameters, such as return on investment (ROI) and greenhouse gas (GHG) emissions; (2) develop a DGEI optimization model to solve energy investment problems that the operating expenses are minimal in each considered investment option; (3) implement the DGEI optimization model using the IBM Optimization Programming Language (OPL) with historical and projected energy demand data, i.e., electricity, heating, and cooling, to solve energy investment optimization problems; and (4) conduct an experimental case study for a university campus microgrid and utilize the DGEI optimization model and its OPL implementations, as well as the analytical and graphical methodology to make an investment decision and to measure tradeoffs among cost savings, investment costs, maintenance expenditures, replacement charges, operating expenses, GHG emissions, and ROI for all the considered options.
UR - http://www.scopus.com/inward/record.url?scp=84927741355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84927741355&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-09492-2_10
DO - 10.1007/978-3-319-09492-2_10
M3 - Conference contribution
AN - SCOPUS:84927741355
T3 - Lecture Notes in Business Information Processing
SP - 154
EP - 173
BT - Enterprise Information Systems - 15th International Conference, ICEIS 2013, Revised Selected Papers
A2 - Cordeiro, José
A2 - Filipe, Joaquim
A2 - Cordeiro, José
A2 - Filipe, Joaquim
A2 - Maciaszek, Leszek A.
A2 - Hammoudi, Slimane
A2 - Maciaszek, Leszek A.
PB - Springer Verlag
T2 - 15th International Conference on Enterprise Information Systems, ICEIS 2013
Y2 - 4 July 2013 through 7 July 2013
ER -