Grid Optimization Techniques For Ensuring Efficient Energy Distribution – Assessing the quality of heart rate variability assessed by wrist and finger PPG: a new approach based on the cross-mapping method

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Grid Optimization Techniques For Ensuring Efficient Energy Distribution

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What Is A Smart Grid? What Are The Major Smart Grid Technologies?

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Energy Optimization Techniques For Your Retail Store

Author: Ghulam Hafeez Ghulam Hafeez Scilit Preprints.org Google Scholar 1, 2, Zahid Wadud Zahid Wadud Scilit Preprints.org Google Scholar 3, Imran Ullah Khan Imran Ullah Khan Scilit Preprints.org Google Scholar 4, *, Imran Khan Imran Khan Scilit Preprints Print org Google Scholar 2, Zeeshan Shafiq Zeeshan Shafiq Scilit Preprints.org Google Scholar 2, Muhammad Usman Muhammad Usman Scilit Preprints.org Google Scholar 5 and Mohammad Usman Ali Khan Mohammad Usman Ali Khan Scilit Preprints.org Google Scholar 6

Date received: 7 April 2020/Date revised: 26 May 2020/Date accepted: 26 May 2020/Date published: 2 June 2020

As the demand for electrical energy from a rapidly growing world population increases exponentially, the world will experience electricity shortages in the future. With the development of the Internet of Things (IoT), more intelligent devices will be integrated into residential buildings in smart cities, actively participating in the electricity market through demand response (DR) programs, and effectively managing energy to meet the growing energy demand. Therefore, under this motivation, an energy management strategy using a price-based disaster recovery plan is developed for IoT-enabled residential buildings. We propose a wind-driven bacterial foraging algorithm (WBFA), which is a hybrid of wind-driven optimization (WDO) and bacterial foraging optimization (BFO) algorithms. Next, we develop a strategy based on the proposed WBFA to systematically manage the power consumption of IoT-enabled residential building smart devices through planning to mitigate the peak-to-average ratio (PAR), minimize energy costs, and maximize user comfort (UC). ) ). This increases the efficient use of energy, thereby increasing the sustainability of IoT residential buildings in smart cities. The WBFA-based strategy automatically responds to price-based DR plans to solve the main problem of DR plans, which is the limitation of consumer knowledge in receiving DR signals. To support the productivity and efficiency of the proposed WBFA-based strategy, extensive simulations were conducted. In addition, the proposed WBFA-based strategy is compatible with benchmarks including binary particle swarm optimization (BPSO) algorithm, genetic algorithm (GA), genetic wind-driven optimization (GWDO) algorithm, and genetic binary particle swarm optimization (GBPSO) algorithm. . were compared between energy consumption, electricity cost, PAR and UC. Simulation results show that the proposed WBFA-based strategy outperforms the baseline strategy in terms of performance metrics.

Pdf) What Is The Smart Grid? Definitions, Perspectives, And Ultimate Goals

With the rapid growth of the population and the economic development, people are increasingly dependent on electric power and the energy consumption continues to increase. To further emphasize, the authors documented that electricity demand will increase up to 40% in the energy sector and 25% in the commercial and residential sectors by 2025 [1]. The outdated grid cannot cope with increasing power demand and contemporary challenges such as hybrid generation, two-way communications and two-way electricity flows. Therefore, the modern power grid, i.e., Smart Grid (SG), has evolved to accommodate the Internet of Things (IoT), modern control technologies, information and communication technologies (ICT), bidirectional power flow, and hybrid power. generation To address this growing electricity demand, SGs can actively participate in either of two programs: installing power plants or broadcasting question answer (DR) programs on energy management [2].

The DR scheme is SG’s key incentive program aimed at convincing consumers to participate in the electricity market through Advanced Metering Infrastructure (AMI). There are two types of disaster recovery plans: (a) incentive-based disaster recovery plans and (b) price-based disaster recovery plans. In (a), the Distribution System Operator (DSO) is an IoT-enabled agent that can remotely monitor the consumer’s equipment at short notice when needed. In (b), IoT-enabled users spontaneously manage their electricity usage based on the provided price-based incentives [3]. Since residential buildings consume more than 80% of energy (a large part of total energy), (b) is an imperative program that leads to positive results for DSOs and consumers when performing energy management [4].

In disaster recovery planning, one of the challenges is the lack of user knowledge, which hinders user participation [5]. One of the developed solutions is the adoption of automated controllers at user locations that actively participate and contribute to solving optimization problems, called Energy Management Controllers (EMC). When EMC is used together with IoT, consumers’ energy costs will be effectively minimized without sacrificing UC, which is an incentive for end users to participate in DR initiatives [6]. EMC production is the best power solution for smart equipment in residential buildings. In addition, smart devices, plug-in hybrid electric vehicles (PHEVs), renewable energy sources (RES), and energy storage systems can penetrate into residential buildings to improve sustainability [ 7 , 8 ]. Therefore, home plug-in hybrid electric vehicles and energy storage systems help consumers store energy from renewable sources during the day and discharge it at night, thus obtaining many benefits from their investment. However, achieving the goals requires high capital costs. The authors in references [9, 10, 11] proposed power dispatching strategies for residential building energy management. The strategies developed can effectively reduce electricity costs as well as peak power demand. Furthermore, in these works, active user participation is attracted due to cost minimization without sacrificing UC. The authors introduced a new concept of user priority in energy management systems by scheduling power consumption through DR procedures [12, 13, 14, 15, 16]. Prioritized home devices as well as thermal and operating limits enable EMC to prioritize devices on and off.

Applications Of Optimization Models For Electricity Distribution Networks

The above literature provides sufficient research related to the issue of effective energy management in SG. Although some studies focus on cost minimization, some focus on reducing peak demand, some focus on mitigating peak-to-average ratio (PAR), and some address UC. To our knowledge, none of the above studies have fully exploited the IoT-enabled environment of AMI, DR initiatives, and SGs to simultaneously satisfy the needs of both users and DSOs. Therefore, in this study, we utilize the IoT-enabled environment of AMI, DR initiatives, and SG for effective energy management of residential buildings in smart cities to simultaneously minimize costs, reduce PAR, and maximize UC. DSO satisfaction. The highlights and features of this study are as follows:

The remainder of this manuscript is organized as follows: first, Section 2 discusses related work. Section 3 discusses the proposed energy management framework. Section 4 describes energy management through planning problem description and formulation. Section 5 describes the proposed and baseline strategies. Extensive simulations are performed and their results are discussed in Section 6. Finally, in Section 7, the manuscript is summarized and research directions are also provided as future work.

In SG, in the field of energy management, a large amount of literary work has been done to cope with the increasing electricity demand. Literary work related to this topic is divided into the following categories: (a) energy management based on mathematical models, (b) energy management based on metaheuristics and heuristic methods, and (c) energy management based on hybrid methods. This classification is for better understanding. A detailed proof is as follows:

Ensembles Of Realistic Power Distribution Networks

In Reference [20], the author developed linear programming (LP) for scheduling battery charging/discharging and smart home appliance operations using DAPS and RTPS DR programs to facilitate consumers to reduce electricity bills and maximize UC. Reference [21] developed an energy system mechanism based on integer LP (ILP) to reduce costs and mitigate peak loads. The developed model is a hybrid PV and grid architecture serving residential building loads. However, the desired goals are obtained at the cost of increased complexity and longer execution time. A new mixed integer non-LP (MINLP) based residential load scheduling mechanism is developed for efficient energy management

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