Project

Smart Grid

Goal: Energy Optimization in Smart Grid

Date: 2 February 2016 - 2 February 2018

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Project log

Sakeena Javaid
added 2 research items
Energy management of residential buildings plays an important role in a smart grid. Region specific fuzzy logic strategies are proposed recently. However, no such approach exists that covers all regions of the world. A fuzzy logic-based strategy for the construction of fuzzy controller covering the entire globe would be cost effective due to the increasing power of micro-controllers. Results show that our proposed approach achieves a minimum energy savings of 6.5%, irrespective of where it is used around the world. This research will provide a model for extending the region specific solutions for a worldwide adaption. INDEX TERMS Energy management, thermostat, smart grid, fuzzy logic, fuzzy inference systems.
Sakeena Javaid
added 2 research items
Energy management of residential buildings plays an important role in a smart grid. Region specific fuzzy logic strategies are proposed recently. However, no such approach exists that covers all regions of the world. A fuzzy logic based strategy for the construction of fuzzy controller covering the entire globe would be cost effective due to the increasing power of micro-controllers. Results show that our proposed approach achieves a minimum energy savings of 6.5%, irrespective of where it is used around the world. This research will provide a model for extending the region specific solutions for a worldwide adaption.
In this paper, a new orchestration of Fog-2-Cloud based framework is presented for efficiently managing the resources in the residential buildings. It is a three layered framework having: cloud layer, fog layer and consumer layer. Cloud layer is responsible for the on-demand delivery of the resources. Effective resource management is done through the fog layer because it minimizes the latency and enhances the reliability of cloud facilities. Consumer layer is based on the residential users who fulfill their daily electricity demands through fog and cloud layers. Six regions are considered in the study, where, each region has a cluster of buildings varying between 80 to 150 and each building has 80 to 100 homes. Load requests of the consumers are considered fixed during every hour in the complete day. Two control parameters are considered: clusters of buildings and load requests, whereas, three performance parameters: request per hour, response time and processing time are also included. These parameters are optimized by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. The simulation results show that our proposed technique has outperformed the previous techniques in terms of the aforementioned parameters. Tradeoff occurs in the processing time of the algorithms as compared to response time and request per hour.
Sakeena Javaid
added a research item
In this research work, two energy management controllers are proposed: swarm optimization fuzzy mamdani (SOFM) and swarm optimization fuzzy sugeno (SOFS) for the efficient scheduling and controlling of the electric loads in a residential building which is comprised of 10 apartments having the single family setup. Two types of electric loads are considered in terms of: daily used appliances and the seasonally used appliances. In addition, two demand side management strategies are used for managing both type of loads concerning to the load shifting and load curtailment. Daily used electric loads are the commonly used appliances considered in the residential buildings; whereas in the seasonally used electric loads, only airconditioning systems are considered. Load scheduling technique (binary particle swarm optimization) is applied for the scheduling of daily used electric loads whereas load curtailment strategy (fuzzy logic) is applied for the seasonally used electric loads in order to manage the load in an effective fashion. The input parameters are: number of appliances, time-slots, power rating, length of operation time and utility price in case of the daily used electric loads; whereas in case of the seasonally used electric loads, we considered the following input parameters: initialized setpoints, user occupancy, price ratings, indoor and outdoor temperature. Output parameters considered for both types of the electric loads are: energy consumption (EC), cost, peak to average ratio (PAR) and energy efficiency. Simulations are performed in Matlab and results proved that our proposed controllers: SOFM and SOFS outperformed the unscheduled and existing approaches in terms of minimizing EC, cost, PAR and energy efficiency. SOFM outperformed to the existing and unscheduled approach till 45% and 48% in terms of minimizing EC and cost reduction.
Sakeena Javaid
added 2 research items
Controlling power utilization in the residential area is one of the major challenges in the smart grid (SG). Demand response (DR) has played a vital role in energy management and improved it with the involvement of residential consumers who participate in such programs from utilities for scheduling their appliances to the off peak hours. In this paper, we have proposed a worldwide adaptive thermostat model for effectively managing the power in all countries of the world. The proposed approach has been evaluated with the help of fuzzy logic and its two inference systems (FIS): 1) Mamdani and 2) Takagi Sugeno. Utilizing the membership functions; outdoor temperature, user occupancy, utility price and initialized setpoints are evaluated for maintaining the buildings temperature. Furthermore, energy consumption in buildings is analyzed by tuning the indoor initialized setpoints while considering all the aforementioned parameters. Simulations are conducted in Matlab to validate the proposed system model and results show that energy consumption in cold countries is reduced upto 45% as compared to the existing programmable approach. Index Terms—Energy management, thermostat, smart grid, fuzzy logic, takagi sugeno fuzzy inference system, mamdani fuzzy inference system
In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is used to calculate electricity bill. Our goals are electricity cost reduction, energy consumption minimization, Peak to Average Ratio (PAR) minimization, and user comfort maximization. However, there is trade-off between multiple objectives (goals). The simulation results show that, there is trade-off between PAR and total cost, and there is trade-off as well between PAR and waiting time. The simulation results also show that CSA performs better in terms of total cost and user comfort than EDE and unscheduled.
Sakeena Javaid
added a research item
In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
Sakeena Javaid
added a research item
Home Energy Management System (HEMS) enhances the load scheduling in the next-generation electric grid. Residential users send responses to utilities for scheduling their appliances to the off peak hours when prices are low. The scheduling of the household appliances still not succeeded too much by having some drawbacks. In this research, we have proposed a new algorithm namely GASC for scheduling by using superclustering of appliances and their working timing hours. This algorithm is developed by using the GA for appliance clustering and scheduling. It is validated by the simulations which were conducted for this procedure.
Sakeena Javaid
added a project goal
Energy Optimization in Smart Grid