Muhammad Hassan Rahim

Comparative Assessment of Performance for Home Energy Management Controller in Smart Grid

This paper, provides comparative assessment of performance for home energy management (HEM) controller which categories the household appliances into three different categories 1) Fixed appliances 2) Interrupt able appliances and 3)Non-interrupt able appliances on the bases of their load profiles and user preference. It is designed on the bases of two bio-inspired algorithms, genetic algorithm (GA), bacterial for aging algorithm (BFA) and two nature-inspired algorithms binary particle swarm optimization algorithm (BPSO) and ant colony optimization algorithm (ACO). Demand side management system(DSM) is also inaugurate. Real time pricing (RTP) model is used for energy price calculation. The objectives of minimize electricity cost consumption and peak to average (PAR) ratio are achieve successfully, as simulations validates. Simulations perform foraforemention heuristic algorithms, ACO perform best amongall four algorithms. Average cost for schedule algorithms GA, BFA, BPSO and ACO are 95.58%, 81%, 90.4% and 76.48%respectively.

Weighted Cuckoo Search Based Load Balanced Cloud for Green Smart Grids

The concept of cloud computing is becoming popular with each passing day. Clouds provide virtual environment for computation and storage. Number of cloud users is increasing drastically which may cause network congestion problem. To avoid such situation, fog computing is used along with cloud computing. Cloud act as a global system and fog works locally. As the requests from users are increasing so load balancing is also required on fog side. In this paper, a three layered cloud and fog based architecture is proposed. Fog computing acts as a middle layer between users and the cloud. Users’ requests are handled at fog layer and filtered data is forwarded to cloud. A single fog has multiple virtual machines (VMs) that are assigned to the users’ requests. The load balancing problem of these requests is managed by proposed weighted cuckoo search (WCS) algorithm. Simulations are carried out to evaluate the performance of proposed model. Results are presented in the form of bar graphs for comparison and detailed values of each parameter are presented in tables. Results show the effectiveness of proposed technique.

A Hybrid Technique for Residential Load Scheduling in Smart Grids Demand Side Management

Demand side management (DSM) and demand response (DR) are the key functions in smart grids (SGs). DR provides an opportunity to a consumer in making decisions and shifting load from on-peak hours to off-peak hours. The number of incentive base pricing tariffs are established by a utility for the consumers to reduce electricity consumption and manage consumers load in order to minimize the peak to average ratio (PAR). Throughout the world, these different pricing approaches are in use. Time of use tariff (ToU) is considered in this paper, to comparatively evaluate the performance of the heuristic algorithms; bacterial foraging algorithm (BFA), and harmony search algorithm (HSA). A hybridization of BFA and HSA (HBH) is also proposed to evaluate the performance parameters; such as electricity consumption cost and PAR. Furthermore, consumer satisfaction level in terms of waiting time is also evaluated in this research work. Simulation results validate that proposed scheme effectively accomplish desired objectives while considering the user comfort.

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.

Home Energy Management in Smart Grid Using Bacterial Foraging and Strawberry Algorithm

The electricity demand from residential buildings is increasing gradually day by day. Home Energy Management Systems (HEMS) are used to meet this demand by using Demand Sides Management (DSM) to reduce the pressure on consumers and utility companies. In this paper, HEMS is facilitated by using different meta-heuristic scheduling techniques: The Strawberry Algorithm (SBA) and Bacterial Foraging Algorithm (BFA). The SBA is useful for every kind of optimization problem and helps in scheduling the electricity load. To compute the cost efficiently Time-of-Use (ToU) pricing scheme is used. Results illustrate that the cost is reduced efficiently along with Peak to Average Ratio (PAR).

The Trends of Integrating Renewable Energy Sources: A Survey

Renewable energy sources (RESs) are considered as future replacement of traditional energy generation sources with zero carbon emission and low price electricity producers. RESs are intermittent, uncertain and random in nature, they do not produce fixed amount of energy and heavily depend upon weather, season and area. In this paper, new trends in the integration of photovoltaic and wind turbine are presented. This paper discusses the integration of RESs at three level i.e. consumer level, micro grid level and main grid level. A comprehensive review of the intermittent and stochastic nature of RESs is also provided. Additionally, fault protection concerns and the feasibility of RESs are discussed. Moreover, the usage of storage system to deal with the fluctuating behavior of RESs is presented.

Swarm Intelligence Based Home Energy Management Controller Under Dynamic Pricing Scheme

Energy management controller (EMC) is widely adopted for residential load management in smart grid (SG). Its main focus is on minimizing cost with minimal consumer interaction. EMC effectiveness is improved in the context of demand response (DR) program. In the era of demand side management (DSM) EMC plays a significant role in residential energy management by curtailing the peak to average ratio (PAR) and electricity bill. In this research work, we are reducing the electricity cost and PAR by maximizing the user comfort. In our research work, we consider a smart home with three smart appliances and schedule these appliances energy consumption and user comfort by using heuristic techniques: binary particle swarm optimization (BPSO) and firefly algorithm (FA). Simulations are conducted by utilizing the day-ahead real time pricing (DA-RTP). Simulation results depict that firefly algorithm (FA) has better performance as compared to BPSO in term of minimizing cost and PAR, while also maximizing user satisfaction level. A trade-off analysis between user satisfaction and energy consumption cost is demonstrated in simulations.

Demand Side Optimization in Smart Grid Using Harmony Search Algorithm and Social Spider Algorithm

Electricity is a valuable resource. With the increase of population, this valuable resource is being used inefficiently. To overcome this problem, electricity providers use various techniques like introducing different pricing schemes. In peak hours, when the usage of electricity is high, the utility increases the per unit cost. Therefore, usage of electricity in peak hours result in high electricity bills. The electricity bills can be reduced by efficiently scheduling the home appliances so that few appliances are operated during peak hours. For this purpose many techniques have been proposed. In this paper, we propose a Social Spider Algorithm (SSA) for Demand Side Management (DSM). Harmony Search Algorithm (HSA) has been adapted to evaluate the results of SSA. These algorithms schedules the appliances in such a way that the usage of electricity in peak hours is reduced. This results in reduction of electricity bill and Peak to Average Ratio (PAR).

Energy Efficiency Using Genetic and Crow Search Algorithms in Smart Grid

Demand Side Management (DSM) is an efficient and robust strategy for energy management, Peak to Average Ratio (PAR) reduction and cost minimization. Many DSM techniques have been proposed for industrial, residential and commercial areas in last years. In this paper, we have design Home Energy Management Scheduler (HEMS) using two algorithms Genetic Algorithm (GA) and Crow Search Algorithm (CSA) for electricity cost and PAR minimization. Real Time Pricing (RTP) signals are used for electricity bill calculation. Simulation results demonstrate that our proposed scheme efficiently achieved our targeted objectives. However, GA performs superior than CSA due to high convergence rate. Furthermore, a trade-off exists between electricity cost and user waiting time; when electricity cost is low, user waiting time is high and vice versa.