[Context and Motivation] Global energy consumption has been steadily increasing in recent years, with data centers emerging as major contributors. This growth is largely driven by the widespread migration of applications to the Cloud, alongside a rising number of users consuming digital content. Dynamic adaptation (or self-adaptive) approaches appear as a way to reduce, at runtime and under certain constraints, the energy consumption of software applications. [Question/Problem] Despite efforts to make energy-efficiency a primary goal in the dynamic adaptation of software applications, there is still a gap in understanding how to equip these self-adaptive software systems (SAS), which are dynamically adapted at runtime, with effective energy consumption monitoring tools that enable energy-awareness. Furthermore, the extent to which such an energy consumption monitoring tool impacts the overall energy consumption of the SAS ecosystem has not yet been thoroughly explored. [Methodology] To address this gap, we introduce the EnCoMSAS (Energy Consumption Monitoring for Self-Adaptive Systems) tool that allows to gather the energy consumed by distributed software applications deployed, for instance, in the Cloud. EnCoMSAS enables the evaluation of energy consumption of SAS variants at runtime. It allows to integrate energy-efficiency as a main goal in the analysis and execution of new adaptation plans for the SAS. In order to evaluate the effectiveness of EnCoMSAS and investigate its impact on the overall energy consumption of the SAS ecosystem, we conduct an empirical study by using the Adaptable TeaStore case study. Adaptable TeaStore is a self-adaptive extension of the TeaStore application, a microservice benchmarking application. For this study, we focus on the recommender service of Adaptable TeaStore. Regarding the experiments, we first equip Adaptable TeaStore with EnCoMSAS. Next, we execute Adaptable TeaStore by varying workload conditions that simulate users interactions. Finally, we use EnCoMSAS for gathering and assessing the energy consumption of the recommender algorithms of Adaptable TeaStore. To run these experiments, we use nodes of the Grid5000 testbed. [Results] The results show that EnCoMSAS is effective in collecting energy consumption of software applications for enabling dynamic adaptation at runtime. The observed correlation between CPU usage and energy consumption collected by EnCoMSAS provides evidence supporting the validity of the collected energy measurements. Moreover, we point out, through EnCoMSAS, that energy consumption is influenced not only by the algorithmic complexity but also by the characteristics of the deployment environment. Finally, the results show that the impact of EnCoMSAS on the overall energy consumption of the SAS ecosystem is comparatively modest with respect to the entire set of the TeaStore applications microservices.