Development of a prototype model for the planning of hybrid renewable energy polygeneration systems in off-grid communities

• Main Authors: Buan, Michael Renz Gueco, Hauschild, Melanie Joy Viray, Nunag, Amanda Louise Salay
• Format: text
• Language: English
• Published: Animo Repository 2022
• Subjects: Polygeneration systems; Renewable energy sources; Chemical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etdb_chemeng/10; https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1016&context=etdb_chemeng
• Institution: De La Salle University

Hybrid Renewable Energy Polygeneration Systems (HRES) emerge as one of the solutions being developed to progress energy sustainability. These systems play an essential role in transitioning from nonrenewable energies to renewable energies (RE) since it provides a gradual transition between conventional sources of energy and RE. Literature review found several software utilized to optimize hybrid polygeneration systems with each having their own limitations from the types of energies and technologies considered to their accessibility. Often, knowledge on programming and data analysis are needed to operate the software and interpret the results. Thus, this work develops an optimization model for the design of hybrid renewable energy polygeneration systems that meets the energy demand of off-grid communities. The interface is integrated with an energy database and created using Visual Basic for Applications (VBA) in Excel. Furthermore, the problem utilized mixed integer linear programming via Solver with an objective of either maximizing profit or minimizing CO2 emissions associated with the system designed by the software. A Philippine case study was conducted by selecting ARMM and Region V as the reference regions based on their Multidimensional Energy Poverty Index (MEPI). Six scenarios were created where each region had a wet period, dry period, and multiperiod scenario. For the single-objective optimization, maximization of profit was set as the objective function. The demands for all six scenarios were satisfied with an overall observation that the interface prioritizes the technologies with lower CO2 emissions and lower costs. For the bi-objective optimization via 𝜀-constraint method, Pareto-optimal solutions were successfully generated at varying CO2 emissions. Technologies selected by the systems and their corresponding output capacities varied per scenario, consistently leaving out the electric chiller for single period and turbine 2 for multiperiod which was mainly attributed to how other technologies being considered produced the energy demands more efficiently at lower CO2 emissions.