Abstract
The transformation of a conventional power system to a smart grid has been underway over the last few decades. A smart grid provides opportunities to integrate smart homes with renewable energy resources (RERs). Moreover, it encourages the residential consumers to regulate their home energy consumption in an effective way that suits their lifestyle and it also helps to preserve the environment. Keeping in mind the techno-economic reasons for household energy management, active participation of consumers in grid operations is necessary for peak reduction, valley filling, strategic load conservation, and growth. In this context, this paper presents an efficient home energy management system (HEMS) for consumer appliance scheduling in the presence of an energy storage system and photovoltaic generation with the intention to reduce the energy consumption cost determined by the service provider. To study the benefits of a home-to-grid (H2G) energy exchange in HEMS, photovoltaic generation is stochastically modelled by considering an energy storage system. The prime consideration of this paper is to propose a hybrid optimization approach based on heuristic techniques, grey wolf optimization, and a genetic algorithm termed a hybrid grey wolf genetic algorithm to model HEMS for residential consumers with the objectives to reduce energy consumption cost and the peak-to-average ratio. The effectiveness of the proposed scheme is validated through simulations performed for a residential consumer with several domestic appliances and their scheduling preferences by considering real-time pricing and critical peak-pricing tariff signals. Results related to the reduction in the peak-to-average ratio and energy cost demonstrate that the proposed hybrid optimization technique performs well in comparison with different meta-heuristic techniques available in the literature. The findings of the proposed methodology can further be used to calculate the impact of different demand response signals on the operation and reliability of a power system.
Highlights
The increasing costs of energy and environmental contamination are major concerns in today’s world [1]
The simulation results and discussions are presented to evaluate the performance of a demand-side management (DSM) in the presence of utility, energy storage system (ESS), and PV units
A home energy management system (HEMS) model was proposed for residential electricity consumers using multiple appliances with an ESS and PV generation including the option of a H2G energy exchange
Summary
The increasing costs of energy and environmental contamination are major concerns in today’s world [1]. The increased utilization of fossil fuels is resulting in global warming, which is a challenging issue to deal with These seminal drivers have led to the belief that the need for public and private decision-makers to transition toward green and sustainable energy is inexorable. The integration of renewable energy resources (RERs), especially solar and wind, is a handy option for generating green electricity with reduced global warming effects [2,3]. These factors lead us to the concept of a smart grid, which is a digital, bidirectional network of distributed generators. It relies on intelligent control devices to establish an efficient communication between the consumer and utility provider
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