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Assessing system costs for power generation is essential for evaluating the economical aspect of energy resources. This paper examines traditional and renewable energy resources under uncertainty and variability of input variables. The levelized cost of electricity (LCOE) of each technology is computed using a global sensitivity analysis. A Monte Carlo approach is utilized to study the thermoeconomics of a variety of power generation methods in the United States: fossil fuel-based, nuclear, developed renewable, and emerging renewable energy resources. The results of this study demonstrate how uncertainties in input data can significantly influence the LCOE values. Power generation from well-developed energy technologies exhibit less variability in LCOE due to established capital costs, operating and maintenance costs, and power generation. On the contrary, emerging renewable energy technologies are subject to high uncertainties in both technical and economic performance, as expected for technologies in early stages of development. A scenario with carbon pricing in power generation is also carried out in the paper. The presence of carbon pricing significantly increases the LCOEs of fossil fuel technologies, and LCOEs of other technologies also experience significant changes when life-cycle carbon assessments are considered. Several cost reduction opportunities are discussed to guide the development of future energy conversion, especially from emerging renewable energy resources. [ABSTRACT FROM AUTHOR]

Energy demands, environmental impacts of energy conversion, and the depletion of fossil fuels are constant topics of discussion in the energy industry. Renewable energy technologies have been proposed for many years to address these concerns. However, the transformation from traditional methods of power generation, usually based on fossil fuels, to power generation based on renewable resources presents many challenges associated with emerging, or less established, technologies. This paper examines the role of renewable energy in the U.S. and its potential to meet current and future energy needs in a way that is technically and economically sound. Renewable energy technologies, ranging from well-developed and established to new and emerging technologies, are presented in terms of their technical potential, current state of the technology, potential for further growth, and economic potential. While renewable energy sources are abundant across the U.S., issues of dispatchability, variability, scalability, energy storage, geographic limitations, and investment costs are critical in determining future progress. The analysis in this paper can be used to guide the integration of renewable energy systems toward becoming a larger share of energy production. [ABSTRACT FROM AUTHOR]

Osmotic power with pressure retarded osmosis (PRO) is an emerging renewable energy option for locations where fresh water and salt water mix. Energy can be recovered from the salinity gradient between the solutions. This study provides a comprehensive feasibility analysis for a PRO power plant in a hypersaline environment. A sensitivity analysis investigates the effects of key technical and financial parameters on energy and economic performances. A case study is developed for the Great Salt Lake in Utah, USA (which has an average 24% salt concentration). A 25 MW PRO power plant is investigated to analyze the necessary components and their performances. With currently available technologies, the power plant would require 1.54 m 3 /s (24,410 GPM) fresh water flow rate and 3.08 m 3 /s (48,820 GPM) salt water flow rate. The net annual energy production is projected to be 154,249 MWh, with capital cost of $238.0 million, and operations and maintenance cost of $35.5 million per year. The levelized cost of electricity (LCOE) would be $0.2025/kWh, but further design improvements would reduce the LCOE to $0.1034/kWh. The high salinity of the Great Salt Lake is a critical factor toward making the osmotic power plant economically feasible. [ABSTRACT FROM AUTHOR]

District energy systems (DES) can reduce CO 2 emissions associated with buildings while meeting the energy needs of a group of buildings with fossil fuel or renewable energy resources that are located on-site. One of the present challenges of DES is optimizing the operation of energy components, as different optimization methods are available. These optimization methods can have various requirements for implementation, distinct needs for engineering labor, and may rely on freely accessible software or proprietary software. Most importantly, different methods may result in dissimilar operation planning for a given DES, which makes the selection of optimization method a key consideration for decision-makers. In this study, two optimization methods, a mixed-integer linear programming (MILP) solver as a classical method and a non-dominated sorting genetic algorithm II (NSGA-II) as a metaheuristic method, are used to optimize the early-stage operation planning of a hypothetical DES for a university campus in a cool and dry climate. The objective is to minimize the operating cost and CO 2 emissions when considering uncertainties in energy demands, solar irradiance, wind speed, and annualized electricity-related emissions. Both methods present similar operation of energy components, operating cost, and operating CO 2 emissions. The MILP solver and NSGA-II algorithm vary in computation time to perform the optimization, initial knowledge to run the simulation, accessibility (free/open-source status), and satisfaction of constraints. This work compares the characteristics of a MILP solver and NSGA-II algorithm to help future researchers select the suitable optimization method related to their case study. The software underlying this work is open-source and publicly available to be reused and customized for early-stage operation planning of their specific DES. This work is novel by optimizing the operation planning of a mixed-used DES to minimize the cost and CO 2 emissions while considering uncertainties in weather parameters, energy demands, and annualized electricity-related emissions. • Multi-objective optimization of district energy system performed by MILP and NSGA-II. • Uncertainties in energy demands, meteorology, and emissions are considered. • Results of operation planning are similar between MILP and NSGA-II. • Highly variable renewable sources do not cause high variability in cost or emissions. • An open source framework is presented to help optimize district energy systems. [ABSTRACT FROM AUTHOR]