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Technically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes. [ABSTRACT FROM AUTHOR]

This article proposes a novel stochastic framework based on point estimate method (PEM) and firefly algorithm (FA) for the optimal operation of plug-in electric vehicles (PVs) in the distribution systems. The stochastic problem will consider the uncertainties of active and reactive loads, the number of PVs in the fleets and the departure and arrival time at charging locations. According to the high complexity and severity of the problem, a new version of FA called θ-FA is used to search the solutions in the polar space instead of the traditional Cartesian space. The objective function is to minimize the total cost of the system considering the operation and security constraints. The feasibility and high performance of the propose problem are examined on a standard IEEE test system. [ABSTRACT FROM AUTHOR]

Plug-in Electric Vehicles (PEVs) will play significant role in the future smart grids. In this regard, the increasing appearance of PEVs can create new challenges in the optimal operation of these devices. In this way, this paper suggests a new method for optimal coordination of PEVs for reducing the total cost of the system during the day. The proposed method makes use of the idea of vehicle-to-grid (V2G) for shifting the energy demand in the grid. The problem is then formulated in an intelligent framework based on bat algorithm (BA) and Monte Carlo method to be solved optimally. Meanwhile, we suggest a new modification method for BA to improve its search ability for optimal coordination of PEVs. The proposed problem is examined on the IEEE test system with five PEV fleets. [ABSTRACT FROM AUTHOR]