Anila Yasmeen

Home Energy Management System Using Ant Colony Optimization Technique in Microgrid

From previous years, the research on usage of renewable energy sources (RES), specially photo voltaic (PV) arrays. This paper is based on home energy management system (HEMS). We propose a grid connected microgrid to fulfill the load demand of residential area. We have consider fifteen homes with six appliance for each home, the appliances are taken as the base load. For bill calculation, real time pricing (RTP) tariff is used. Ant colony optimization (ACO) is used for the scheduling of appliances. To fulfill the load demand; Wind turbine (WT), PV, micro turbine (MT), fuel cell (FC) and diesel generator (DG) are used. Energy storage devices are used with generators to store excessive energy. Also, we propose penalty and incentive (PI) mechanism to reduce the overall cost. Objectives of the paper are cost and peak to average ratio (PAR). The simulation results show better performance with our optimization technique rather than without any technique.

Fog Computing Based Energy Management System Model for Smart Buildings

In this article, a three layered architecture is proposed for smart buildings. A fog based infrastructure is designed and deployed on the edge of network, where fog processes the private data collected through the smart meters and stores the public data on cloud. Further, end user has facility to schedule and control the home appliances by using a centralized energy management system. Moreover, the electricity and network resources utilization charges can be calculated. We analyze the performance of cloud based centralized system, considering the fog computing as an intermittent layer between system user layer and cloud layer and without considering fog computing. Simulation results prove that fog layer enhances the efficient utilization of network resources and also reduces the bottleneck on the cloud computing.

A Hybrid Bat-Crow Search Algorithm Based Home Energy Management in Smart Grid

In smart grid (SG), demand side management (DSM) is a set or group of programs, allow consumers to play a vital role in transferring of their own load demand during peak time periods and minimizing their hourly based power consumption and total monetary cost of the electricity consumed and it also helps the electric utility in reducing higher power demand in the time of high energy demanded time slots. Where, this consequently results in reduction of the total electricity cost, maximization of power grid sustainability and reduction in carbon dioxide emissions which ultimately results in a pollution free environment. Nowadays, most of the DSM strategies available in existing literature concentrate on house hold appliances scheduling to decrease consumer delay time and peak to average ratio (PAR). However, they ignore the total electricity cost. In this paper, we employ load shifting strategy, to decrease total electricity payment. To gain above objective, we propose a hybrid of bat algorithm (BA) and crow search algorithm (CSA) i.e., bat-crow search algorithm (BCSA) and the results are compared with the existing BA and CSA. Simulations were conducted for a single home with 15 appliances, uses critical peak pricing (CPP) scheme for the computation of consumer’s electricity bill. The results show that load is successfully shifted to lower price time slots using our proposed BCSA technique, which ultimately leads to 31.191% reduction in total electricity payment.

Optimal Energy Management in Microgrids Using Meta-heuristic Technique

The energy crisis and greenhouse gas emission are increasing around the world. In order to overcome these problems, distributed energy resources are integrated which introduce the concept of microgrid (MG). The microgrid exchanges power with utility to meet load demand with the help of common coupling point. An energy management strategy is proposed in this work, which helps to minimize the operating cost of MG while considering all constraints of the system. For this purpose, a firefly algorithm is used to schedule generators of MG to fulfill the consumer demand considering the desired objectives. The proposed scheme employs FA to minimize the operating cost of a MG. In both grid-connected and islanded modes of MG, proposed scheme is applied for scheduling of distributed generators. The Significance of the proposed strategy is verified through simulations and results.

A Metaheuristic Scheduling of Home Energy Management System

Smart grid (SG) provides a prodigious opportunity to turn traditional energy infrastructure into a new era of reliability, sustainability and robustness. The outcome of new infrastructure contributes to technology improvements, environmental health, grid stability, energy saving programs and optimal economy as well. One of the most significant aspects of SG is home energy management system (HEMS). It encourages utilities to participate in demand side management programs to enhance efficiency of power generation system and residential consumers to execute demand response programs in reducing electricity cost. This paper presents HEMS on consumer side and formulates an optimization problem to reduce energy consumption, electricity payment, peak load demand, and maximize user comfort. For efficient scheduling of household appliances, we classify appliances into two types on the basis of their energy consumption pattern. In this paper, a meta-heuristic firefly algorithm is deployed to solve our optimization problem under real time pricing environment. Simulation results signify the proposed system in reducing electricity cost and alleviating peak to average ratio.

Optimized Energy Management Strategy for Home and Office

In smart grid, Demand Side Management (DSM) plays a vital role in dealing with consumer’s demand and making communication efficient. DSM not only reduces electricity cost but also increases the stability of the grid. In this regard, we introduce an energy management system model for a home and office, then propose efficient scheduling techniques for power usage in both. This system schedule the appliances on the basis of four different optimization techniques to achieve objectives that are electricity cost minimization, reduction in Peak to Average Ratio and energy consumption management. Moreover, we use Real Time Pricing because it is highly flexible and provides an understanding to consumer about price signal variations. Simulation results show that the proposed model for energy management work efficiently to achieve the objectives and provide cost-effective solution to increase the stability of smart grid.

Exploiting Meta-heuristic Technique for Optimal Operation of Microgrid

A power system with different types of micro-sources are very popular in recent years. The aim of the paper is to make the environment green by reducing green house gases and meet the load demand in an efficient way. However, we propose a grid-connected microgrid system which meets the load demand in an efficient manner to achieve our objectives. The objective of this work is to find the optimal set points of controllable micro-sources in terms of cost minimization. The grid-connected microgrid also helps to exchange power with utility during different intervals of a day to meet the load demand. The significance and performance of the proposed strategy is proved through performing simulations in MATLAB. However, the overall cost of MG is less, while in schedulable microsources the cost of FC is less as compared to MT and DE.

Fuzzy Energy Management Controller for Smart Homes

Energy management plays a vital role in maintaining sustainability and reliability of smart grid. It also helps to prevent blackouts. Energy management at consumers side is a complex task. Utility provides incentives like: demand response, time of use and real time pricing models to encourage consumers to reduce electricity consumption in certain periods of time. However, changing energy consumption pattern according to these incentives becomes difficult for consumers. In this paper, we have proposed a fuzzy logic based energy management controller (EMC) for illumination system management. We have used fuzzy logic for reduction of monetary cost and energy consumption. This fuzzy based controller is fully automatic and alters illumination levels between comfort zone of a consumer. It alters illumination level according to price and other input parameters.

Power Management in Smart Grid for Residential Consumers

In this paper we have studied the power management for residential area. A proper load management brought fruitful results in term of Peak to Average Ratio (PAR) reduction and electricity cost. In order to achieve these objectives, we provide an energy management structure to perform scheduling on the basis of Genetic Algorithm (GA) and Fish Swarm Optimization (FSO). Time Of Use (TOU) pricing scheme has been used to calculate electricity cost. After experiments a noticeable difference has been found in the performance of our proposed algorithms GA and FSO. GA provides us better results in term of energy consumption and PAR reduction as compared to FSO. However, FSO performs more efficiently than GA in term of electricity cost reduction.

A Hybrid Genetic Based on Harmony Search Method to Schedule Electric Tasks in Smart Home

In the residential area, high electricity demand of power-consuming household tasks has become a crucial issue. Thus, the key objectives of home energy management system (HEMS) are scheduling power-consuming household tasks to minimize electricity cost and maximize consumer’s comfort. The many utilities offer residential demand response (DR) program to shift residential customer electricity consumption during the peak time period to match demand and supply. In this paper, we propose optimal load scheduling algorithm a hybrid genetic based on harmony search (HGHS) for HEMS to schedule power-consuming household tasks. The new optimal load scheduling algorithm HGHS gives optimal solution to schedule power-consuming household tasks based on real time pricing (RTP) electricity tariff within electricity task time window during the day in order to minimize electricity cost, reduce peak-to-average ratio (PAR) and maximize user comfort. The proposed model implemented in a single smart home and simulation results of proposed HGHS algorithm are compared with genetic algorithm (GA) and harmony search algorithm (HAS) and it provides better results in reducing the daily electricity cost and PAR by reducing load at peak hours. Simulation results shown that the trade-off between electricity cost reduction and user comfort exist in two conflicting objectives.