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Smart Grid

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Anila Yasmeen
added 2 research items
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 consumers 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.
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.
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Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission , consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem (MKP) to mitigate the rebound peaks. The autonomous energy management controller (EMC) proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule (OSR), which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.
Anila Yasmeen
added 3 research items
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.
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 signi�cant aspects of SG is home energy management system (HEMS). It encourages utilities to participate in demand side management programs to enhance e�ciency 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 e�cient 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.
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.