Adia Khalid

Demand Side Management Using Hybrid Bacterial Foraging and Genetic Algorithm Optimization Techniques

Today, energy is the most valuable resource, new methods and techniques are being discovered to fulfill the demand of energy. However, energy demand growth causes a serious energy crisis, especially when demand is comparatively high and creates the peak load. This problem can be handled by integrating Demand Side Management (DSM) with traditional Smart Grid (SG) through two way communication between utility and customers. The main objective of DSM is peak load reduction where SG targets cost minimization and user comfort maximization. In this study, our emphasis is on cost minimization and load management by shifting the load from peak hours toward the off peak hours. In this underlying study, we adapt hybridization of two optimization approaches, Bacterial Foraging (BFA) and Genetic Algorithm (GA). Simulation results verify that the adapted approach reduces the total cost and peak average ratio by shifting the load on off peak hours with very little difference between minimum and maximum 95% confidence interval.

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 Technique for Residential Load Scheduling in Smart Grids Demand Side Management

Demand side management (DSM) and demand response (DR) are the key functions in smart grids (SGs). DR provides an opportunity to a consumer in making decisions and shifting load from on-peak hours to off-peak hours. The number of incentive base pricing tariffs are established by a utility for the consumers to reduce electricity consumption and manage consumers load in order to minimize the peak to average ratio (PAR). Throughout the world, these different pricing approaches are in use. Time of use tariff (ToU) is considered in this paper, to comparatively evaluate the performance of the heuristic algorithms; bacterial foraging algorithm (BFA), and harmony search algorithm (HSA). A hybridization of BFA and HSA (HBH) is also proposed to evaluate the performance parameters; such as electricity consumption cost and PAR. Furthermore, consumer satisfaction level in terms of waiting time is also evaluated in this research work. Simulation results validate that proposed scheme effectively accomplish desired objectives while considering the user comfort.

Cuckoo Search Optimization Technique for Multi-objective Home Energy Management

Increasing demand of power and emergence of smart grid has gain maximum attention of researchers which has further opened new opportunities for Home Energy Management System (HEMS). HEMS under Demand Response (DR) helps to reduce the On-peak hour load by shifting the load toward the Off-peak hours. This load shifting strategy effects the user comfort, however in return DR gives them incentives in term of electricity bill reduction. Consumer electricity cost and peak load have a tradeoff, to sort out this situation an efficient system is required. In this paper, we present a multi-objective HEMS to schedule home appliances using Cuckoo Search Algorithm (CSA) while considering the objective load fitness criteria. This proposed load fitness criteria effectively reduces the cost and peak load. Simulations are performed to verify the generic behavior i.e., system performance on any price tariffs. For this purpose, results are validated for three price signals: day-ahead Real Time Peak Price (RTP), Time of Use (TOU) and Critical Peak Price (CPP).

Demand Side Management Using Meta-Heuristic Optimization Techniques

In this paper, we present a Home Energy Management System (HEMS) using two meta-heuristic optimization techniques: Bacterial Foraging Optimization Algorithm (BFOA) and Bat Algorithm (BA). HEMS will provide different services to end user to manage and control their energy usage with time of use. The proposed model used for load scheduling between peak hour and off-peak hour. In this regard, we perform appliances scheduling to manage the frequent demand from the consumer. The aim of the proposed scheduling is to minimize peak to average ratio and the cost while having some trade-off in user comfort to achieve an optimal management of load. Simulation results show that the BA outperform than BFOA in selected performance parameters.

Pigeon Inspired Optimization and Bacterial Foraging Optimization for Home Energy Management.

In this paper, we are dealing with Home Energy Management System (HEMS) using Bacterial Foraging Optimization (BFO) and Pigeon Inspired Optimization (PIO) techniques in a single home. Performance of Both techniques is evaluated through simulations in term of reduction in electricity cost, Peak to Average Ratio (PAR) by scheduling smart appliances. We have used Critical Peak Pricing (CPP) as a pricing signal and we have gained electricity cost reduction upto 40%.

Real Time Pricing Based Appliance Scheduling in Home Energy Management Using Optimization Techniques

In this paper, appliance scheduling scheme is proposed for residential area. Different types of heuristic and meta-heuristic optimization techniques are being used to solve the general problem of electricity demand. In this paper, a unique swarm based optimization technique Elephant Herding Optimization (EHO) is used to manage the electricity demand in order to manage the single home appliances in such a way that reduction of electricity cost is achieved and certain point of user comfort. For this purpose Real Time Pricing (RTP) scheme is used in this paper for electricity cost. To validate the effectiveness of proposed scheme simulations are performed. The results of EHO are compared with the results of Enhanced Differential Evolution (EDE). The simulations show that proposed scheme i.e. EHO provide best optimal results in achieving the minimum electricity cost and user comfort at certain point.

An Efficient Home Energy Management Scheme Using Cuckoo Search

Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.

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.