Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

With increasing renewable energy utilization, the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans. This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas. First, we provide a detailed formulation to calculate the renewable energy demand based on total energy demand. Second, we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems, after which we apply the differential evolution algorithm to solve the optimization result. Finally, we conduct a case study in Qingdao, China, to demonstrate the effectiveness of this optimization model. Compared to the baseline design, the proposed model reduced annual costs and annual carbon emissions by 14.39% and 72.65%, respectively. These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions. Overall, this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.