Nusrat Shaheen

An Efficient Genetic Algorithm Based Demand Side Management Scheme for Smart Grid

In this paper, we propose a novel strategy for a Demand Side Management (DSM) in a Smart Grid (SG). In this strategy, three types of loads are considered, i.e., residential load, commercial load and industrial load. The larger number of appliances of different power rating for each type of load is considered in this work. The focus of this work is to minimize the Peak to Average Ratio (PAR) to increase the efficiency of SG, by increasing the utilization of spinning reserves. On the other hand, our aim is to minimize the electricity consumption cost. Tackling the large number of appliances in an SG is a challenging task, because it increases the complexity of the problem. However, in literature the focus is on small number of appliance. In this work, the load scheduling problem is mathematically formulated and solved by using genetic algorithm. The simulation results show that the propose algorithm reduces the cost, while reducing the peak load demand of the SG.

The energy crisis in Pakistan: A possible solution via biomass-based waste

Developing countries like Pakistan need a continuous supply of clean and cheap energy. It is a very common fear in todays world that the fossil fuels will be depleted soon and the cost of energy is increasing day-by-day. Renewable energy sources and technologies have the potential to provide solutions to long-standing energy problems faced by developing countries. Currently, Pakistan is experiencing a critical energy crisis and renewable energy resources can be the best alternatives for quickly terminating the need for fossil fuels. The renewable energy sources such as solar energy, wind energy, and biomass energy combined with fuel cell technology can be used to overcome the energy shortage in Pakistan. Biomass is a promising renewable energy source and is gaining more interest because it produces a similar type of fuel like crude oil and natural gas. Energy from biomass only depends upon the availability of raw materials; therefore, biomass can play an important role to fulfill the energy requirements ...

Towards Multiple Knapsack Problem Approach for Home Energy Management in Smart Grid

The energy demand of residential end users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. In this paper, we describe a scheme to solve multiple knapsack problems (MKP) using heuristic algorithms. Keeping total energy consumption of each household appliance under certain threshold with maximum benefit is regarded as knapsack problem. Here, we design multiple knapsack problems for each hour of a day to schedule different numbers of appliance. To avoid peak hours, load is shifted in low and mid peak hours. Different algorithms are used to schedule household appliances. We use ant colony optimization (ACO) that is one of the meta-heuristic techniques to solve multiple knapsack problems which enables fast convergence rate for scheduling of appliances. Results show that propose scheme is an efficient method for home energy management to maximize user comfort and minimize electricity bills.

Ant Colony Optimization Based Energy Management Controller for Smart Grid

In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives, it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.

Peak Load Shaving Model Based on Individual's Habit

Smart Grid is supposed to play an important role in future energy management. In smart grid, Demand Side Management (DSM) is one of the main areas which is under focus of researchers in order to solve the classical problem of peak demand management. In this paper, we have proposed a Habit Based DSM (HBDSM) model for peak load shaving. Proposed method is based on individuals habit which is modeled using Markov Chain. The main focus of the work is to minimize the cost by optimizing battery consumption using Equal Interval Search algorithm in order to minimize energy consumption from the grid and so as to shave the demand curve. Simulation results prove the effectiveness of the proposed model.

A Hybrid Algorithm for Energy Management in Smart Grid

Efficient energy management requires smart approaches in demand side as well as demand response management assisted by smart, innovative, and computationally feasible schemes. Artificial intelligence algorithms are increasingly becoming helpful in generating multiple scenarios for a range of real world problems on the pattern of human Intelligence. This paper draws on employing algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) to generate a hybrid algorithm inspired by the characteristics of these two. Finally simulations have been drawn graphically to compare the energy consumption patterns for appliance scheduling schemes as well as corresponding cost analysis for energy optimization.

Real-Time Pricing with Demand Response Model for Autonomous Homes

Smart Grid (SG) is a next-generation electrical power system that use two way communication in the generation, consumption and delivery of the electrical energy. One of the key feature of SG is Demand Response (DR). In DR a pricing signal is provided to the customer via smart meters, and customer modifies their demand in response to price signals. However, most of the load scheduling schemes used day-ahead or Time of Use pricing scheme, these schemes are deviating from Real-time Pricing (RTP) scheme. In this paper, an RTP based scheduling scheme is proposed using Optimal Stopping Rule (OSR). AnOSR gives the best operating time of the device to reduce electricity bills. The cost minimization problem is formulated as an unconstrained optimization problem. Moreover, waiting time cost is also formulated as sub problem to reduce waiting time of the device. Waiting time is considered as a function of cost. After that, an algorithm is proposed to solve this optimization problem for various types of loads. Simulation results verify that proposed algorithm has low computational complexity and reduce electricity bill with less waiting time.

Appliance Scheduling for Energy Management with User Preferences

Demand Side Management (DSM) technique encourages the consumers to adjust their energy usage pattern to get optimized results for achieving the goal of minimizing the electricity consumption cost. This mechanism provides benefits to both side customer and utility in terms of cost reduction and grid stability. To regulate the increasing energy demand extensive research is being carried out for possible implementation of different DSM techniques. DSM technique ensures the user participation for achieving the energy optimization goal. User preferences and comfort is much desirable while achieving the goal of reducing Peak to average ratio (PAR), energy cost reduction and grid stability. In this paper we are proposing an Energy Management System (EMS) by considering different user environments along with the users preferences for shifting the appliances operational time. We have selected home and office environments for implementing our EMS. We are using GA, a heuristic technique, to solve the energy management problem.