Itrat Fatima

Energy Efficient Integration of Renewable Energy Sources in Smart Grid

With the emergence of smart grid (SG), the residents have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management (DSM). In this regard, we design energy management control unit (EMCU) based on genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind driven optimization (WDO) to schedule appliances in presence of objective function, constraints, control parameters, and comparatively evaluate the performance. For energy pricing, real time pricing (RTP) plus inclined block rate (IBR) is used. RESs integration to SG is a challenge due stochastic nature of RE. In this paper, two techniques are addressed to handle the stochastic nature of RE. First one is energy storage system (ESS) which smooths out variation in RE generation. Second one is the trading/cooperation of excess generation to neighboring consumers. The simulation results show that WDO perform more efficiently than unscheduled in terms of reduction in: electricity cost, the tradeoff between electricity cost and waiting time, and peak to average ratio (PAR). Moreover, incorporation of RESs into SG design increase the revenue and reduce carbon emission.

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.

Towards Real-Time Opportunistic Scheduling of the Home Appliances Using Evolutionary Techniques

The tremendous evolution of the technology has empowered the energy consumers to receive real-time information regarding electricity consumption prices with the help of two way communication between the main grid and the smart meter. We have proposed evolutionary optimization techniques such as; genetic algorithm (GA) and teaching-learning base algorithm (TLBO) in this paper. The aforementioned algorithms are exploited to find out an optimal schedule for every appliance based on real-time pricing (RTP) signal. It enables the real-time automation of smart home appliances considering the economic criteria of each smart home. Our scheduling strategy shifts the extra load exceeding the threshold limit to the hours where the electricity pricing is low. In this way, we can reduce electricity cost while considering the user comfort by reducing delay and peak to average ratio (PAR).

Region Oriented Integrated Fog and Cloud Based Environment for Efficient Resource Distribution in Smart Buildings

In this paper, an integrated fog and cloud based environment for effective energy management is proposed in which fogs are connected to cloud in order to reduce the burden of cloud. It handles the data of clusters of buildings at consumers’ end. Six fogs are used on six different regions in the world which are based on six continents. Furthermore, each fog is connected to cluster of buildings and one fog is connected to one cluster. Each cluster comprises of multiple smart buildings and these buildings has at least 100 smart homes. Microgrids (MGs) are available near the buildings and accessible by the fogs. Energy is managed for these homes and fog helps the consumers to fulfill their load demands through nearby MGs and cloud servers’ communication. The requests are sent by the homes or buildings to the fog according to the energy demands and fog forwards these requests to nearby MGs to fulfill them. The MGs establish the connection and provide electricity to relevant homes in the building and requests are managed by the round robin algorithm. Proposed model is evaluated in terms of demand request time, demand response time and demand processing time and it performs efficiently during the peak demand periods.

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.

Stochastic Power Management in Microgrid with Efficient Energy Storage

In order to mitigate the extra cost and to reduce the energy consumption, distributive power system are widely accepted in recent years. The reason of adaptation of distributive power system is the scalability of power supply and demand which helps in reliable power supply and optimizes the annual expenditures. Moreover, the integration of power distributive systems with renewable energy sources enabled the optimal utilization of photovoltaic arrays for effective and cost efficient power supply. To exploit the integration of distributive power and renewable sources, we solve the power dispatch problem with heuristic optimization techniques. We have performed scheduling for supply side management. For this purpose, we have formulate our problem using chance constrained optimization and transformed the problem into mixed integer linear programming. Finally, simulation results demonstrate that the proposed scheduling method for microgrid performs efficiently and effectively.

User Satisfaction Based Home Energy Management System for Smart Cities

With the advent of smart grid and demand side management techniques, users have opportunity to reduce their electricity cost without compromising their comfort much. In this paper, we evaluate the performance of home energy management system based on user satisfaction. Our objective is to maximize the total user satisfaction within user defined budget. For budget three different scenarios are presented that are;

Swarm Intelligence Based Home Energy Management Controller Under Dynamic Pricing Scheme

0.25/day,

Home Energy Management Using Fish Swarm Optimization Bacterial Foraging Algorithm and Genetic Algorithm in Smart Grid

0.50/day and

An Efficient Home Energy Management Scheme Using Cuckoo Search

1.00/day. To obtain the desired satisfaction three optimization techniques are used: genetic algorithm (GA), enhanced differential evolution (EDE) algorithm, harmony search algorithm (HSA) and their results are compared in terms of achieved satisfaction.