Energy Allocation Strategies for Micro-Grids

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, producers, and the power grid. Two-way communication in MGs offers the opportunity to allocate the produced energy inside a community of consumers, and, as a result, make the energy flow less dependent on the central grid. However, challenges arise regarding energy sharing, namely: (i) how to balance the demand and supply inside communities; (ii) how to dictate the impact –for the community– of serving the needs of a household; and (iii) how to balance the economic benefit –under a policy– for everyone who participates. In this thesis, we propose energy allocation algorithms for MG communities consisting of households that use renewable sources of energy (RSEs). Our objective is to maximize the usage of the energy created by the producers inside the community and minimize the cost, 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. Since these groups share similar energy trends, we can decide the (group of) consumers that should be served first or that should accept higher amounts of energy than the rest, by dictating consumer priority policies (CPPs). Then, after defining the value of serving each consumer inside the community (by imposing a CPP), we create energy allocation strategies (EASs). These are algorithms which define the way in which the produced energy will be distributed, based on the already imposed CPP. 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 the cost of trading energy within the MG and requesting energy from 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 benefit economically by allocating the excess of energy.