Power production plans are used by power companies to propose a feasible plan regarding the load requirements and system conditions of the following year, in which the arrangement of production material, such as the procurement of fuel required and delivery schedules, is clearly projected. Some of the factors involved in considering power production plans are common to those for short-term unit commitment, including system power balance, system security requirements, and unit characteristics; however, the limit on annual accumulation, which is not considered in short-term unit commitment, is not similar between the two. This study proposes an optimal mathematical model based on mixed-integer programming for long-term unit commitment to minimize the total system costs. To avoid the mixed-integer linear programming problems that increase the duration of computation because of numerous integer variables, the model proposed in this study applies the view of energy and power and integrates the similarities of load patterns and fuzzy logic to reduce the number of variables while adhering to all the constraints. The proposed algorithm can greatly improve the solution time compared with the complete UC model for the Taipower one week simulation case.

The number of Distributed generators is currently increasing, and the electrical industry is trending toward regional supply-and-demand and resource integration. Thus, a model that can forecast small-area peak electrical loads is an indispensable part of power infrastructures. This study constructs innovative model for forecasting small-area peak electrical loads. The main aspects considered were the accuracy of the forecasting model and the convenience of follow-up maintenance and management of the model and data. This study used yearly peak load value and total power data from substations to construct regression tree models. These acted as models for the small-region peak electrical load of substation districts in the Taipower distribution systems. The errors of these forecasting models were substantially smaller than those of the least squares model originally used by Taipower to forecast peak load. The addition of exogenous factors was unnecessary. Additionally, our results were superior regardless of whether once or incremental models were adopted for the data. This confirms the usability of our models.

The impact of climate change and the pressure of environmental awareness have caused the concepts of energy conservation, carbon reduction, and the sustainable engagement of a production model involving green ecology to become common development goals in power utilities worldwide. This study examines the recycling application framework of microalgal carbon fixation developed by Taipower, which has been used for the past decade, and evaluates the framework using a social survey on the social acceptability of the biotech skincare products that were exploited by microalgal carbon fixation. Research results show that Taipower’s engagement in carbon fixation using microalgae is practicable in reducing carbon, providing an additional production model of circular economy to power business development. The application of microalgal carbon fixation is projected to have numerous benefits, such as tangible profit benefit through future mass production, the intangible benefit from the image of enterprise environmental protection, and the benefit through social promotion. Therefore, focus on the sustainable development of microalgal carbon fixation is worthwhile. This study provides a guideline for future development of the recycling application of microalgal carbon fixation at Taipower and other power utilities.