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Automatic weather stations (AWSs) are widely used for an environmental sensing in harsh environments such as Antarctica, high mountains, and deserts. As these systems are often deployed far from mains power sources, they are usually equipped with rechargeable batteries and energy harvesting systems. Predeployment configuration of an AWS is a challenging task, as designers have to face with contrasting energy-related choices, the correct tradeoff of which determines the success of the AWS’s mission and its survivability. Among them, the most effective are the energy harvesting technology, size of the battery, and frequency of sensing and communication. In this paper, we describe AENEAS, an energy-aware simulator of AWSs that allows designers to assess the impact of hardware and software choices on the energy evolution of the system. The tool is extensively configurable, thus enabling the simulation of a large number of hardware configurations as well as of the sensing and communication applications running on the AWS. The simulator has been validated by comparing results computed by AENEAS with data collected from two real-world AWSs installed on an alpine glacier and in a urban environment, obtaining a high accuracy in both cases. A number of use cases are discussed to demonstrate how AENEAS can be used to assess the impact on the energy behavior of the AWS of different batteries, energy harvesters, and application behaviors.