Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques

Abstract

The main problem for both the utility companies and the end-used is to efficiently schedule the home appliances using energy management to optimize energy consumption. The microgrid, macro grid, and Smart Grid (SG) are state-of-the-art technology that is user and environment-friendly, reliable, flexible, and controllable. Both utility companies and end-users are interested in effectively utilizing different heuristic optimization techniques to address demand-supply management efficiently based on consumption patterns. Similarly, the end-user has a greater concern with the electricity bills, how to minimize electricity bills, and how to reduce the Peak to Average Ratio (PAR). The Home Energy Management Controller (HEMC) is integrated into the smart grid, by providing many benefits to the end-user as well to the utility. In this research paper, we design an efficient HEMC system by using different heuristic optimization techniques such as Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO), to address the problem stated above. We consider a typical home, to have a large number of appliances and an on-site renewable energy generation and storage system. As a key contribution, here we focus on incentive-based programs such as Demand Response (DR) and Time of Use (ToU) pricing schemes which restrict the end-user energy consumption during peak demands. From the results figures, it is clear that our HEMC not only schedules all the appliances but also generates optimal patterns for energy consumption based on the ToU pricing scheme. As a secondary contribution, deploying an efficient ToU scheme benefits the end-user by paying minimum electricity bills, while considering user comfort, at the same time benefiting utilities by reducing the peak demand. From the graphs, it is clear that HEMC using GA shows better results than WDO and BPSO, in energy consumption and electricity cost, while BPSO is more prominent than WDO and GA by calculating PAR.

A Sustainable Pattern of Waste Management and Energy Efficiency in Smart Homes Using the Internet of Things (IoT)

“…Step (1) The first step of the fuzzy hierarchical analysis process method, such as the deterministic AHP method, is drawing the hierarchical structure or hierarchical state of the research problem. It should be noted that this issue has only two levels of goals and main indicators.…”

“…Due to the increase in waste production and the concerns related to the resulting ecological damage [1], waste management policy has become extremely important [2]. Waste is a by-product of human activities in urban areas, and residential homes are one of these areas [3].…”

“…This makes it possible to accurately identify the components of energy distribution and production monitored and measured. Therefore, this may pose the challenge of whether the use of the IoT was ultimately able to create a waste management model for optimizing energy consumption in smart homes [1,6]. Therefore, the present study pursues objectives such as: (a) determining the pattern of waste management and energy efficiency in smart homes; (b) determining the impact of energy costs on smart homes; (c) determining the impact of the number of home residents in smart homes and determining the impact of home area on smart homes using the IoT technologies.…”

A Multi-Objective Improved Cockroach Swarm Algorithm Approach for Apartment Energy Management Systems

“…Needed more accuracy. [30] The authors suggested employing heuristic optimization techniques to schedule the Home Energy Management Controller (HEMC) effectively.…”