The advances of the information and communication technology (ICT) brought changes in the energy distribution domain, introducing the Smart Grid (SG). In SG, generators, distributors, and consumers communicate in a bidirectional way. SGs are envisaged to include micro-grids (MG) consisting of distributed control networks of consumers, prosumers, and the power grid. Two-way communication in MGs allows allocating the produced energy inside a community of consumers, to decentralize the energy flow. However, challenges arise regarding energy sharing, namely: (i) how to balance the demand and supply inside communities; (ii) are there policies that prioritize among the consumers while distributing the producers’ excess of energy; and (iii) how to balance the economic benefit –under a policy– for everyone who participates. In this thesis, we propose energy allocation strategies (EAS) for MG communities consisting of households that use renewable sources of energy (RSEs). Our objective is to maximize the energy usage and the cost reduction, under certain priority policies. Through an in-depth analysis of energy and socioeconomic data of the community, we form groups of households that share similar characteristics, and we channelize the energy flow at will. We present seven, simple and optimized, EASs and several consumer priority policies (CPPs). Our EASs and CPPs are scalable and can meet the specific needs of an MG community. We evaluate our algorithms and techniques using real data, acquired from a community of 443 households over a year. We show that the groups of households that we prioritize cover their needs of energy, sometimes completely, in periods of high energy production. We compare on economic basis trading energy within the MG and requesting energy form the grid (classic way). The expenses for prioritized groups of consumers under our EASs are decreased, up to 50% in certain cases. Further, it is shown that even the non-prioritized consumers are benefited economically by allocating the excess of energy.