Information and Communication Technology (ICT) is now touching various aspects of our lives. The electricity grid with the help of ICT is transformed into Smart Grid (SG) which is highly efficient and responsive. It promotes twoway energy and information flow between energy distributors and consumers. Many consumers are becoming prosumers by also producing energy. The trend is to form small communities of consumers and prosumers leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow by allocating the produced energy within the community. Energy allocation amongst them needs to solve issues viz., (i) how to balance supply/demand within micro-grids; (ii) how allocating energy to a user affects his/her community. To address these issues we propose six Energy Allocation Strategies (EASs) for MGs - ranging from simple to optimal. We maximize the usage of the energy generated by prosumers within MG. We form household-groups sharing similar characteristics to apply EASs by analyzing thoroughly energy and socioeconomic data of households. We propose four metrics to evaluate EASs. We test our EASs on the data from 443 households over a year. By prioritizing specific households, we increase the number of fully served households up to 81 compared to random sharing.

Artificial lighting is a pervasive element in our daily lives. Researchers from different communities are investigating challenges and opportunities related to artificial lighting but from different angles: energy disaggregation, to monitor the status of light bulbs in buildings; and communication, to transmit information wirelessly. We argue that there is an unexplored synergy between these two communities. When a light bulb modulates its intensity for communication, it also affects the current it draws. This current signature is unique and could be used by energy disaggregation methods to identify the lights' status. These signatures however will be exposed to interference (collisions of signatures) and distortions due to power line effects. To overcome these problems, we build upon coding schemes to assign interference-resilient signatures, and we develop custom hardware to ameliorate distortions introduced by power lines. We validate our framework in a proof-of-concept testbed, perform simulations to test scalability, and use energy traces from real homes to evaluate the impact of other electric loads.