Asif Khan

Improved link to system interfacing methodology for exponential effective SNR mapping using enhanced lookup tables

Link to system interface methodology is a technique used in system level simulations to predict the receiver behavior using lookup tables under different channel conditions. This paper presents an improvement in link to system interfacing methodology for exponential effective signal to noise ratio mapping for a single input and single output transmission mode. It is proposed that the mean value of the receiver performance measure for different channels be used as reference instead of the performance of a single line of sight link. The results show a considerable improvement in the mean square error between the predicted and observed frame error rates at different signal to noise ratios.

Performance Measurement of Energy Management Controller Using Heuristic Techniques

A smart grid is a modernized form of the traditional grid. Smart grid benefits both, consumer and energy services provider. Demand side management is one of the key component of smart grid to fulfill consumers electricity demands in an efficient manner. It helps consumers to manage their load in an effective way to reduce their electricity bill. In this paper, we design a home energy management controller based on three heuristic techniques: teaching learning based optimization, binary particle swarm optimization and enhanced differential evaluation. The major objective of designing this controller is to minimize consumers electricity bill while maximizing consumers satisfaction. Simulation results show that TLBO achieved maximum user satisfaction at minimum cost and peak to average ratio. A tradeoff analysis between user satisfaction and energy consumption cost is demonstrated in simulation results.

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.

Appliances Scheduling Using State-of-the-Art Algorithms for Residential Demand Response

Smart Grid (SG) plays vital role to utilize electric power with high optimization through Demand Side Management (DSM). Demand Response (DR) is a key program of DSM which assist SG for optimization. Smart Home (SH) is equipped with smart appliances and communicate bidirectional with SG using Smart Meter (SM). Usually, appliances considered as working for specific time-slot and scheduler schedule them according to tariff. If actual run and power consumption of appliances are observed closely, appliances may run in phases, major tasks, sub-tasks and run continuously. In the paper, these phases have been considered to schedule the appliances using three optimization algorithms. In one way, appliances were scheduled to reduce the cost considering continuous run for given time slot according to their power load given by company’s manual. In other way, actual running of appliances with major and sub-tasks were paternalized and observed the actual consumption of load by the appliances to evaluate true cost. Simulation showed, Binary Particle Swarm Optimization (BPSO) scheduled more optimizing scheduling compared to Fire Fly Algorithm (FA) and Bacterial Frogging Algorithm (BFA). A hybrid technique of FA and GA have also been proposed. Simulation results showed that the technique performed better than GA and FA.

Optimized Energy Management Strategy for Home and Office

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.

Link level performance prediction of MIMO PIC receivers through QR decomposition of channel matrix

In this paper, an accurate QR decomposition based link level performance abstraction technique for a multi-input multi-output iterative receiver is proposed. The post-detection SNR values for an iterative receiver are estimated using QR decomposition of the channel matrix. The effect of channels on the received SNR is realized by scaling the signal in accordance with the leading diagonal elements of the R matrix. Both mutual and exponential mapping functions have been used for data compression. The proposed link level performance abstraction strategy employs a single lookup table without having to do the complex link level simulations. It is independent of the channel conditions and depends only on the type of the receiver used and the modulation and coding scheme adopted. The resulting link level performance prediction shows a good approximation of the simulated performance at different modulation and coding scheme.

Balancing Demand and Supply of Energy for Smart Homes

Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining balance between demand and supply by implementing demand response (DR). In SG the main focus of the researchers is on home energy management (HEM) system, that is also called demand side management (DSM). DSM includes all responses, which adjust the consumer’s electricity consumption pattern, and make it match with the supply. If the main grid cannot provide the users with sufficient energy, then the smart scheduler (SS) integrates renewable energy source (RES) with the HEM system. This alters the peak formation as well as minimizes the cost. Residential users basically effect the overall performance of traditional grid due to maximum requirement of their energy demand. HEM benefits the end users by monitoring, managing and controlling their energy consumption. Appliance scheduling is integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances or shifting the load from peak to off peak hours. Recently different techniques based on artificial intelligence (AI) are being used to meet aforementioned objectives. In this paper, three different types of heuristic algorithms are evaluated on the basis of their performance against cost saving, user comfort and peak to average ratio (PAR) reduction. Two techniques are already existing heuristic techniques i.e. harmony search (HS) algorithm and enhanced differential evolution (EDE) algorithm. On the basis of aforementioned two algorithms a hybrid approach is developed i.e. harmony search differential evolution (HSDE). We have done our problem formulation through multiple knapsack problem (MKP), that the maximum consumption of electricity of consumer must be in the range which is bearable for utility and also for consumer in sense of electricity bill. Finally simulation of the proposed techniques will be conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of minimum cost, reduced peak to average ratio (PAR), waiting time and equally distributed energy consumption pattern in each hour of a day to benefit both utility and end users.

A Survey of Optimization Techniques for Scheduling in Home Energy Management Systems in Smart Grid

This survey paper is based on comprehensive study of optimization techniques used in smart grid and reviews one of the most popular evolutionary optimization technique i.e., differential evolution (DE) optimization. In addition, different types of DE algorithm currently used in literature are also discussed. These include enhanced DE, modified DE and hybrid DE algorithm. Furthermore, the role of these techniques in solving optimization tasks and scheduling is also discussed.

Low complexity link level performance prediction for SIMO systems

In this paper a low-cost link level performance prediction technique is proposed for a single input and multiple output system. Receiver link level abstraction is used in system level simulations of large networks in order to reduce their complexity. Usually, a single lookup table is employed in link level abstraction to predict a receiver’s performance under different channel conditions. In the presented work, the mean frame error rate curve of different diverse channels is proposed as the reference for performance prediction in single input multiple output systems. Its generation involves compression of the received code word into a single quality measure based on the postdetection signal to noise ratio values using nonlinear capacity, exponential, and mutual mapping functions. The overall performance difference between simulated and predicted frame error rates shows that the proposed scheme gives very good performance approximations under different modulation and coding schemes, clearly outperforming the classical line of sight channel lookup table.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

In the smart grid (SG) users in residential sector adopt various load scheduling methods to manage their consumption behavior with specific objectives. In this paper, we focus on the problem of load scheduling under utility and rooftop photovoltaic (PV) units. We adopt genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), and proposed genetic wind driven optimization (GWDO) algorithm to schedule the operation of interruptible appliances (IA) and non interruptible appliances (Non-IA) in order to reduce electricity cost and peak to average ratio (PAR). For energy pricing combined real time pricing (RTP) and inclined block rate (IBR) is used because in case of only RTP their is possibility of building peaks during off peak hours that may damage the entire power system. The proposed algorithm shift load from peak consumption hours to off peak hours and to hours with high generation from rooftop PV units. For practical consideration, we also take into consideration pricing scheme, rooftop PV units, and ESS in our system model, and analyze their impacts on electricity cost and PAR. Simulation results show that our proposed scheduling algorithm can affectively reflect and affect users consumption behavior and achieve the optimal electricity cost and PAR.