• Energy Research

AbstractTechnically, 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.

The growing adoption of microgrids necessitates efficient management of electrical energy storage units to ensure reliable and sustainable power supply. This paper investigates a thermal management system (TMS) for maintaining the longevity of large-scale batteries. To streamline the thermal modeling of batteries, the McCormick relaxation method is employed to linearize a nonlinear and interdependent heat generation model. The thermal model of the battery follows a nonlinear behavior where the generated heat makes the battery system temperature soar, thereby affecting the thermal performance of the battery. To showcase the efficacy of the proposed approach, four distinct case scenarios are studied, highlighting the critical importance of batteries within microgrid operations. A comparative analysis is conducted between linear and nonlinear models for TMS performance. A quantitative assessment based on simulation results demonstrates the precision of the linearized model, particularly in a multitemporal optimal power flow and day-ahead scheduling of microgrids incorporating energy storage units. Controlling the battery temperature within a permissible range (from 15 °C to 40 °C) is achieved by using a heating, ventilation, and air conditioning (HVAC) system. The paper explores the economic implications of energy storage units in microgrids by extracting and comparing daily operational costs with and without battery integration. The findings reveal that the inclusion of energy storage units yields substantial economic benefits, with potential profit margins of approximately 20 % during typical working days and 60 % on weekends.

AbstractA new approach for optimal demand response program (DRP) in the microgrid considering the high penetration of the solar energy and tidal units as significant and popular renewable sources in the system is proposed here. The proposed method makes use of a multi‐objective problem (MOP) to not only minimize the total operation cost of the scheduling problem but also mitigate the high risk of the interruption in power delivery due to the components failure rate and long repairing rates. Considering the high complexity and nonlinearity of the formulation, a novel heuristic method based on the firefly algorithm is introduced to solve the problem without any assumption or killing the accuracy. In addition, a dynamic three‐phase correction (DPC) formulation is proposed which can help to increase the global search characteristics of the method when boosting the convergence capability of the model. Due to the hard predictability nature of the solar irradiance, a deep learning model based on generative adversarial networks (GAN) is presented to predict the output power of the solar and tidal units properly. The high performance and feasibility of the proposed multi‐layer problem are assessed on an IEEE test system.