Target-oriented robust optimization of a microgrid system investment model

• Main Authors: Uy, Lanz Timothy G., Uy, Patric, Siy, Jhoenson, Chiu, Anthony Shun Fung, Sy, Charlle
• Format: text
• Published: Animo Repository 2018
• Subjects: Microgrids (Smart power grids); Renewable energy sources; Robust optimization; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/387; https://animorepository.dlsu.edu.ph/context/faculty_research/article/1386/type/native/viewcontent
• Institution: De La Salle University

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.