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Urban wastewater service provision is an important energy consumer as well as a potentially important energy producer. This study aims to advance understandings on the influence of climate change on the intra- and inter-annual patterns of wastewater treatment plants’ net life cycle energy consumption. Historic monthly operational data of a wastewater treatment plant in the northeast United States were obtained and its current net life cycle energy demand was investigated. Comprehensive multivariate and multiple linear regression analyses were then performed. The main climate variables (temperature, rainfall, and snowfall) and the wastewater characteristics (flow rate, water temperature, total suspended solids, 5-day biochemical oxygen demand, and chemical oxygen demand) were used to develop regression models for energy that is directly and indirectly consumed and generated at the treatment plant. Two different approaches, a lumped and a month-based method, for conducting the regression analysis were investigated. Whenever possible, these two approaches were combined to improve the predictive power of the regression models. The obtained result shows the treatment plant’s direct energy use consists of more than 86% of the total energy consumption currently. Various energy recovery strategies allow the treatment plant to offset more than 15% of its total energy consumption. The future annual wastewater influent of the plant was projected to decrease towards the end of the century under climate change, with a significantly larger seasonal variation. The influent wastewater quality is projected to decrease, leading to higher direct and indirect energy consumption for treatment. Projections of future intra-annual responses show that the seasonal variations of wastewater flowrate as well as the monthly cumulative energy demand can potentially experience a two-fold increase, resulting in more frequent system shocks and create operational difficulties. 

With the increasing implementation of solar photovoltaic (PV) systems, comprehensive methods and tools are required to dynamically assess their economic and environmental costs and benefits under varied spatial and temporal contexts. This study integrated system dynamics modeling with life cycle assessment and life cycle cost assessment to evaluate the cumulative energy demand, carbon footprint, water footprint, and life cycle cost of residential grid-connected (GC) and standalone (SA) solar PV systems. The system dynamics model was specifically used for simulating the hourly solar energy generation, use, and storage during the use phase of the solar PVs. The modeling framework was then applied to a residential prototype house in Boston, MA to investigate various PV panel and battery sizing scenarios. When the SA design is under consideration, the maximum life cycle economic saving can be achieved with 20 panels with no battery in the prototype house, which increases the life cycle economic savings by 511.6% as compared to a baseline system sized based upon the engineering rule-of-thumb (40 panels and 40 batteries), yet decreases the demand met by 55.7%. However, the optimized environmental performance was achieved with significantly larger panel (up to 300 units) and battery (up to 320 units) sizes. These optimized configurations increase the life cycle environmental savings of the baseline system byup to 64.6%, but significantly decrease the life cycle economic saving by up to 6868.4%. There is a clear environmental and economic tradeoff when sizing the SA systems. When the GC system design is under consideration, both the economic and environmental benefits are the highest when no battery is installed, and the benefits increase with the increase of panel size. However, when policy constraints such as limitations/caps of grid sell are in place, tradeoffs would present as whether or not to install batteries for excess energy storage.