Target-oriented robust optimization of a microgrid system investment model
College
Gokongwei College of Engineering
Department/Unit
Industrial Engineering
Document Type
Article
Source Title
Frontiers in Energy
Volume
12
Issue
3
First Page
440
Last Page
455
Publication Date
9-1-2018
Abstract
An emerging alternative solution to address energy shortage is the construction of a microgrid system. This paper develops a mixed-integer linear programming microgrid investment model considering multi-period and multi-objective investment setups. It further investigates the effects of uncertain demand by using a target-oriented robust optimization (TORO) approach. The model was validated and analyzed by subjecting it in different scenarios. As a result, it is seen that there are four factors that affect the decision of the model: cost, budget, carbon emissions, and useful life. Since the objective of the model is to maximize the net present value (NPV) of the system, the model would choose to prioritize the least cost among the different distribution energy resources (DER). The effects of load uncertainty was observed through the use of Monte Carlo simulation. As a result, the deterministic model shows a solution that might be too optimistic and might not be achievable in real life situations. Through the application of TORO, a profile of solutions is generated to serve as a guide to the investors in their decisions considering uncertain demand. The results show that pessimistic investors would have lower NPV targets since they would invest more in storage facilities, incurring more electricity stock out costs. On the contrary, an optimistic investor would tend to be aggressive in buying electricity generating equipment to meet most of the demand, however risking more storage stock out costs. © 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.
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Digitial Object Identifier (DOI)
10.1007/s11708-018-0563-1
Recommended Citation
Uy, L. G., Uy, P., Siy, J., Chiu, A., & Sy, C. (2018). Target-oriented robust optimization of a microgrid system investment model. Frontiers in Energy, 12 (3), 440-455. https://doi.org/10.1007/s11708-018-0563-1
Disciplines
Operations Research, Systems Engineering and Industrial Engineering
Keywords
Microgrids (Smart power grids); Renewable energy sources; Robust optimization
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