Sundas Shafiq

Optimization of Home Energy Management System Through Application of Tabu Search

In the past few years, a number of optimization techniques have been designed for Home Energy Management System (HEMS). In this paper, we evaluated the performance of two heuristic algorithms, i.e., Harmony Search Algorithm (HSA) and Tabu Search (TS) for optimization in residential area. These algorithms are used for efficient scheduling of Smart Appliances (SA) in Smart Homes (SH). Evaluated results show that TS performed better than HSA in achieving our defined goals of cost reduction, improving User Comfort (UC) level and minimization of Peak to Average Ratio (PAR). However, there remains a trade-off between electricity cost and waiting time.

Cuckoo Optimization Algorithm Based Job Scheduling Using Cloud and Fog Computing in Smart Grid

The integration of Smart Grid (SG) with cloud and fog computing has improved the energy management system. The conversion of traditional grid system to SG with cloud environment results in enormous amount of data at the data centers. Rapid increase in the automated environment has increased the demand of cloud computing. Cloud computing provides services at the low cost and with better efficiency. Although problems still exists in cloud computing such as Response Time (RT), Processing Time (PT) and resource management. More users are being attracted towards cloud computing which is resulting in more energy consumption. Fog computing is emerged as an extension of cloud computing and have added more services to the cloud computing like security, latency and load traffic minimization. In this paper a Cuckoo Optimization Algorithm (COA) based load balancing technique is proposed for better management of resources. The COA is used to assign suitable tasks to Virtual Machines (VMs). The algorithm detects under and over utilized VMs and switch off the under-utilized VMs. This process turn down many VMs which puts a big impact on energy consumption. The simulation is done in Cloud Sim environment, it shows that proposed technique has better response time at low cost than other existing load balancing algorithms like Round Robin (RR) and Throttled.

An Optimal Power Flow Approach for Stochastic Wind and Solar Energy Integrated Power Systems

In this study, an optimal power flow (OPF) problem is formulated for economic operation of electrical system while incorporating stochastic and intermittent nature of solar photovoltaic (PV) and wind generators. The main objective of this study is the minimization of power generation cost and induction of uncertain renewable energy resources into the system while achieving the optimal setting of control variables. Besides, minimization of generation cost, concern on environment is also taken into account and reduction of carbon emission factor is included into the objective function. Genetic algorithm (GA) is used for optimization of an OPF problem. The proposed system is tested on IEEE 30 bus test system and achieved results are compared with literature to highlight the effectiveness of the system.

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 Heuristic Scheduling Approach for Demand Side Energy Management

In this paper, we proposed a mechanism for power scheduling problem in Home Energy Management System using two heuristic algorithms Tabu Search (TS) and Enhanced Differential Equation (EDE) with Real time pricing scheme. The aim of this proposed scheduling mechanism is to find an optimal daily load schedule which can achieve a balanced trade-off between electricity cost and user comfort with respect to management of peak hour load. Simulation results show that TS has achieved a lower waiting time than EDE. Whereas, EDE has performed better than TS in reducing cost. Moreover, according to overall results TS performed better in comparison to other as it achieves a balanced trade-off between cost and user comfort and also avoids peak formation.

Load Scheduling Optimization Using Heuristic Techniques and Combined Price Signal

In this paper, a comparative analysis of two heuristic algorithms, i.e., enhanced differential evolution (EDE) and tabu search (TS) with unschedule load approach for its optimality is proposed. This paper aims to achieve minimum electricity bill and maximum peak to average ratio (PAR) reduction while considering the factor of user satisfaction. In order to achieve our aim, an objective function of electricity cost reduction is made based upon the scheduling strategies. A combined model of pricing schemes, i.e., time of use (ToU) and critical peak pricing (CPP) is used to calculate electricity bill and to tackle the instability. We implemented a state of art user-defined taxonomy of appliances in our paper to deal with the user comfort appropriately in a residential area. Simulation results shows that our proposed strategy works better to encourage the users for intelligent power consumption.