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The evolution towards smart grids (SGs) is mainly characterized by the integration of renewable energy sources (RESs) throughout the grid. The intermittent nature of these sources necessitated the installation of energy storage systems (ESSs) to improve the efficiency and reliability in the power system. Moreover, the ongoing high price of batteries has encouraged the installation of shared ESSs in residential communities. However, managing the shared ESS and the energy flows in the community is considered a key challenge. In order to handle this issue, we introduce a novel energy management system (EMS), namely E nergy M anagement I n residential CO mmunities with shared storage based on multi-agent systems (EMICO). It finds the optimal energy trading operations between households, as well as the operations of the shared ESS that minimize the total energy losses. We first propose a new cluster-based architecture for the residential community in which we integrate Internet of Energy (IoE) devices to manage energy flows and find the shortest path to transfer energy with minimal loss from a cluster to the other. Then, we model our energy management problem as a constrained optimization problem and we use Lagrange multiplier method to solve it in a centralized way. In order to preserve households' privacy, we propose a decentralized approach based on multi-agent systems (MASs) to solve our problem. We test our approach on real data traces obtained from a set of households located in the United Kingdom. Numerical results show that EMICO outperforms a literature approach in terms of energy losses (up to 35.66% of reduction in energy losses), electricity bill (up to 21.21% cheaper), number of exchanged messages (up to 83.81% less messages exchanged), and length of required cables (up to 95.03% less cables required). [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]