Sheraz Aslam

An Efficient Home Energy Management and Power Trading in Smart Grid

In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization.

Scheduling of Appliances in HEMS Using Elephant Herding Optimization and Harmony Search Algorithm

Many techniques have been proposed to manage the demand and supply of electricity. However, due to rapid increase in population, electricity demand becomes a serious issue. In this paper, we evaluated the performance of Home Energy Management System (HEMS) on the basis of two optimizing techniques: Elephant Herding Optimization (EHO) and Harmony Search Algorithm (HSA). For managing demand response, we apply our proposed technique in HEMS for electricity cost and PAR minimization with desirable user waiting time. Simulations results demonstrate the effectiveness of our proposed technique in terms of electricity cost and PAR minimization. However, trade-off occurs between electricity cost and waiting time, when electricity cost is high, than user waiting time is minimized and vice versa.

Efficient Energy Management System Using Firefly and Harmony Search Algorithm.

In this paper, performance of energy management controller (EMC) based on meta-heuristic algorithms: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA) are evaluated. Critical peak pricing (CPP) scheme is implemented to calculate the electricity cost. Appliances are categorized into three groups on the basis of power consumption. Electricity cost minimization and electricity load shifting from peak hours towards off peak hours are the main objectives of the paper. In simulation results, adopted approach reduces the Peak to Average Ratio (PAR) and total electricity cost. Furthermore, HSA shows better results than FA in terms of PAR and electricity cost.

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.

Using Meta-Heuristic and Numerical Algorithm Inspired by Evolution Differential Equation and Strawberry Plant for Demand Side Management in Smart Grid

“Save Energy” is the cry of the day. Energy demand is increasing day by day, on the other hand Energy generation is decreasing, creating a gap between Demand Side Management (DSM) and Supply Side Management (SSM) leading to maximum Peak Average Ratio (PAR) formation. To overcome the gap between DSM and SSM and saving bill cost of the consumers we have designed a consumption scheduling technique for home area in DSM, using Enhanced Differential Equation (EDE) and Strawberry Algorithm (SBA). EDE and SBA schedule user’s appliances intelligently and creates a daily optimal load balance for DSM and SSM thus providing advantage to both consumer and utility. Our proposed technique have not only focused PAR minimization but also User Comfort (UC) maximization, cost reduction, and waiting time minimization. Our simulation results verified that, we have obtain better results than other existing techniques.

An Efficient Home Energy Management Scheme Using Cuckoo Search

Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.