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

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Simultaneous Parameter Tuning of PSS and Wide-Area POD in PV Plant using FPA

In future power grid scenario, large-scale renewable energy based on power plant will be one of the main generations. Among renewable based power plant type, large-scale photovoltaic (PV) plant becoming more popular as they could provide zero emission and sustainable energy. However, even though PV plant could contribute positive impact to the environment, they could also contribute negatively to the power system. Large-scale PV generation came with different dynamic and zero inertia characteristic due to the application of the power electronic devices. Furthermore, PV plant has also drawback in terms of intermittent power output due to the uncertainty of the sources. Those handicaps could deteriorate the stability performance of power system especially oscillatory stability. Adding power system stabilizer (PSS) to the systems is one of the approaches for handling the oscillatory stability. However, with integration of PV plant in the systems, PSS alone is not enough to handle the oscillatory problems coming from various sources such us from PV plant dynamic. Hence, utilizing wide-area power oscillation damping (POD) as PV plant supplementary controller is inevitable. Hence, this paper proposes simultaneous parameter tuning between PSS and wide-area POD in PV plant using flower pollination algorithm (FPA) as the optimization method. The two-area power system is employed to evaluate the performance of PSS and POD using FPA. From the results, it is found that the proposed method could enhance the oscillatory stability of the system

Managing the demand in a Micro Grid Based on Load shifting with Controllable Devices Using Hybrid WFS2ACSO Technique

The Demand Side Management (DSM) introduced in Smart Grid (SG), which depends on load shifting with huge number of devices is presented in this work. The proposed hybrid strategy is the joint implementation of Wingsuit Flying Search (WFSA) algorithm and Artificial Cell Swarm Optimization (ACSO). The searching behavior of WFSA is enhanced by ACSO. Hence, it is named as WFS2ACSO. This technique aims at minimization of electricity bill, power consumption, and Peak Average Ratio (PAR). The daily load change method presented in this manuscript is utilized for defusing the minimization issues. The present method is performed in SG that constitutes three different types of loads on a residential area, a commercial area, and an industrial area. Simulation results demonstrate that the projected DSM methodology achieves considerable savings, as peak load demand of SG decreases. Further, the variation in PAR levels with and without the DSM methodology is also presented. The proposed model is executed on a MATLAB simulation platform with two case studies based on optimization methods like WFSA, WFS2ACSO). The results obtained present the hybridized algorithm effectiveness as compared with other trendsetting optimization techniques like Ant lion optimization (ALO) and particle swarm optimization (PSO).publishedVersio

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Energy storage systems and power conversion electronics for e-transportation and smart grid

The special issue “Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid” on MDPI Energies presents 20 accepted papers, with authors from North and South America, Asia, Europe and Africa, related to the emerging trends in energy storage and power conversion electronic circuits and systems, with a specific focus on transportation electrification and on the evolution of the electric grid to a smart grid. An extensive exploitation of renewable energy sources is foreseen for smart grid as well as a close integration with the energy storage and recharging systems of the electrified transportation era. Innovations at both algorithmic and hardware (i.e., power converters, electric drives, electronic control units (ECU), energy storage modules and charging stations) levels are proposed