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Across Europe, householders have taken the opportunity to produce their electricity, helping them to reduce their electricity bill as well as reducing carbon emissions, by installing rooftop Photovoltaic Panels (PV) on their buildings. New adequate business models are needed for improving the sharing of PV between consumers in a community. Both technical and economic aspects should be considered in a clear way for consumers to understand and benefit from the community. In this paper, an overview of energy communities is provided to support the innovative PV sharing models for buildings, which are proposed in a way to be clear for community members. The developed concepts are supported by the formulation of the optimization problem to be solved by the community manager.

Due to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve's load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem's main objective is to minimize the energy cost according to both machines' energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.

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