A Decision Support Model for Microgrids - An Application to Taichung Industrial Park

• Main Authors: Chang, Chung-Chuan, 張中權
• Other Authors: Jin-Su Kang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/34262569206667805440

碩士 === 國立交通大學 === 企業管理碩士學程 === 100 === The microgrid concept, which encompasses the application of distributed energy resources and renewable energy, has gained arousing interest in recent years due to the increasing concern on environmental sustainability and demand for a more reliable power supply system from the civil society. So far, the pontential benefits of microgrids have not yet been exploited because of the entry barrier caused by the complexity and uncertainty within the microgrid system. A variety of microgrid models have been presented before to optimize the planning of microgrids; however, robust optimization has not been applied to the modeling of microgrids to deal with uncertainties in customer loads, fuel prices, electricity tariff rate, and carbon tax rate, etc. In this study, a mixed-integer linear programming (MILP) model is proposed to adopt an economic robust measure, worst-case cost, as one of the components in the multi-objective function to allow robust optimization of a planned microgrid as early as in the design stage. The model is designed to simultaneously address the issue of expected cost minimization and worst-case cost minimization, which helps in handling the variation among different scenarios of the system operation. With comprehensive mathematical formulation, the model aims at rendering capacity design recommendations for a microgrid from economic, energy-saving, and environmental perspectives. The results of the simulation include the formation of a Pareto curve between the expected cost and worst-case cost of the project, which enables the model users to judge the degree of their risk-taking on microgrid design. The application of the proposed model to Taichung Industrial Park in Taiwan demonstrates the applicability of the model as a decision support tool for microgrid planning.