MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy

Tao Chen, Manisa Pipattanasomporn, Imran Rahman, Zejia Jing and Saifur Rahman

Renewable Energy, 2020, vol. 156, issue C, 1089-1099

Abstract: This paper describes the developed MATPLAN - a probability-based production costing tool that treats renewable energy sources as candidates in a power system expansion planning study. Similar to the structure of the well-known Wien Automatic System Planning (WASP) package, MATPLAN comprises six modules: LOAD-CALC, EXIST-GEN, CANDI-GEN, CONFIG, OPTIMIZE and ELCC. MATPLAN takes into account the variable nature of renewable energy sources, including both solar and wind farms, and allows a user to consider renewable energy as options for expansion planning using a probability-based model. In contrast to the current practice that treats renewable energy sources as negative loads, MATPLAN enables system planners to take renewable sources as normal options for generation expansion planning problems, which can directly determine the optimal expansion policy and ELCC analysis of a system with high renewable penetration. MATPLAN is also designed as an open access software package which academics and practitioners can adapt to their specific situations and run many case studies as a screening tool. In the case studies, the tool was validated by comparing its resulting optimal expansion planning plan against that of the well-known WASP package and further tested using realistic field data. The complete MATPLAN code repository has been released under open-source licenses for public access, URL: https://github.com/wasp2019/MATPLAN. MATPLAN Wiki is also available, providing MATPLAN overview, features, user guides and developer resources.

Keywords: Generation expansion planning; Production costing simulation; Renewable energy integration (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:156:y:2020:i:c:p:1089-1099

DOI: 10.1016/j.renene.2020.04.145

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