A hybrid optimisation decision model for a smart green energy industry park: Exploring the impact of the carbon tax policy in Taiwan. (October 2021) - Yang, Chih-Hao
Highlights: The SGEIP model is formulated as a mixed-integer linear programming (MILP) problem. Smart green energy planning incorporating the LCA into the evaluation process. Considering the energy supply, storage, and saving-categories into decision model. Using ABC techniques for determining maximum scenario project profits. The carbon tax is added into our MILP formulation to explore the influences. Abstract: Smart green energy development is an increasingly pressing public concern for the government advocate policy, as it responds to the challenges of climate change. The national green energy policy can promote the development of optimal electricity portfolios from smart green energy industry parks, thereby ensuring the maximized benefits of energy supply, energy storage, and energy-saving technologies, while accelerating the promotion of green energy industry economics. This study presents a novel 0–1 Mixed Integer Linear Programming (MILP) decision model that focuses on the scenario project costs, the feed-in tariff prices and the quantity of the carbon footprint, in relation to the effects of the tax policy, for determining maximum scenario project profits. The major contributions of this study are as follows: [1] The integrated model can help green energy contractors to understand how to allocate resources and funding for sustainable environmental activities of each scenario project, by using appropriate cost drivers; [2] The obtained portfolio shows the maximum profits for green energy planning and contributes to the development of the national energy policy in Taiwan; and [3] This study contributes to innovative industrial engineering research into the problem of green energy planning, by considering a portfolio with different energy categories, and the results prove the superiority of smart green energy planning that considers a carbon tax policy.