Intelligent Algorithm for Efficient Use of Energy Using Tackling the Load Uncertainty Method in Smart Grid

In this paper, i am developing a unique optimization based real time inland load management algorithm that takes into account load ambiguity in order to minimize the energy payment for each residential user, as well as reduce the peak to average ratio to overcome the drawbacks in the stability of electrical grid. By categorizing the all residential load in different classes, i.e. must run, interruptible and uninterruptible appliances, i used the real time pricing scheme for load management. However, real time pricing creates the peak profiles when the energy demand is too high, that’s why i used the combination of real time pricing and inclining blocks rates model to improve the grid stability by reducing the peak to average ratio. A simulation results show that the proposed algorithm efficiently and effectively reduced the overall residential energy cost as well as peak to average ratio of our model for data provided

Analysis of factors affecting peak to average ratio and mean power in wave energy computer models using regular and irregular waves

This thesis investigates the factors that affect the Peak to Average Ratio (PTAR) and Mean Power (MP) for the Slider Crank Wave Energy Converter (SCWEC). The goal of this thesis is to reduce the PTAR while maximizing the MP. The PTAR needs to be reduced, because the generator that converts wave energy to electricity for the grid would become more efficient and less costly. During the process of minimizing the PTAR, the impact on MP production should be minimized, since producing usable power is the main purpose of any generation mechanism. In this thesis, a few system parameters affecting the PTAR and the MP are investigated and analyzed. In this analysis, these parameters are applied to multiple models of the system, and the results are recorded and compared. It is observed that the best combination of the PTAR and the MP can be determined under regular wave conditions as well as irregular wave conditions. Some of the factors that affect PTAR and MP include phase and time delay. Inertia additionally had an effect on both but was minimal

Low Cost and Reliable Energy Management in Smart Residential Homes Using the GA Based Constrained Optimization

Recently smart grids have given chance to residential customers to schedule operation times of smart home appliances to reduce electricity bills and the peak-to-average ratio through the demand side management. This is apparently a multi-objective combinatorial optimization problem including the constraints and consumer preferences that can be solved for optimized operation times under reasonable conditions. Although there are a limited number of techniques used to achieve this goal, it seems that the binary-coded genetic algorithm (BCGA) is the most suitable approach to do so due to on/off controls of smart home appliances. This paper proposes a BCGA method to solve the above-mentioned problem by developing a new crossover algorithm and the simulation results show that daily energy cost and peak to average ratio can be managed to reduce to acceptable levels by contributing significantly to residential customers and utility companies

Smart Energy Management System for Minimizing Electricity Cost and Peak to Average Ratio in Residential Areas with Hybrid Genetic Flower Pollination Algorithm

Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous regarding appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, ourproposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC

Powerline Communication: Efficient Communication Considerations

This paper focuses on the efficiency considerations while implementing Powerline Communication. The OFDM modulation scheme is deliberated, before introducing the adaptation of Hadamard Coded Modulation. The BER and Peak-to-average ratio (PAPR ) of OFDM are discussed in conjuncture with those of Hadamard Coded Modulation. Further discussions are made on reducing DC Bias without significant Information Loss and concept of interleaving for dispersive channels. Security of system is also preconceived

A Novel Dynamic Appliance Clustering Scheme in a Community Home Energy Management System for Improved Stability and Resiliency of Microgrids

Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained