气候变化领域集成服务门户(CCPortal): An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid

CCPortal

DOI	10.1109/ACCESS.2020.3007095
	An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid
	Nawaz A.; Hafeez G.; Khan I.; Jan K.U.; Li H.; Khan S.A.; Wadud Z.
发表日期	2020
ISSN	21693536
起始页码	132551
结束页码	132581
卷号	8
英文摘要	The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO2) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios. © 2013 IEEE.
英文关键词	advanced metering infrastructure; carbon reduction; demand side management; energy management controller; forecasting; heuristic algorithms; multi-layer perceptron; price-based demand response program; Smart grids
scopus关键词	Advanced metering infrastructures; Carbon dioxide; Cost reduction; Demand side management; Electric power transmission networks; Electric utilities; Electromagnetic compatibility; Energy management; Genetic algorithms; Multilayer neural networks; Particle swarm optimization (PSO); Smart power grids; Bacterial Foraging Optimization Algorithm (BFOA); Binary particle swarm optimization; Demand response programs; Demand side energy managements; Distribution companies; Multi layer perceptron; Optimization framework; Peak to average ratios; Cost benefit analysis
来源期刊	IEEE Access
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/177025
作者单位	Department of Electrical Engineering, University of Engineering and Technology, Mardan, Pakistan; Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 44000, Pakistan; College of Automation, Harbin Engineering University, Harbin, China; Department of Mechatronics Engineering, University of Engineering and Technology Peshawar, Peshawar, Pakistan; Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar, Pakistan
推荐引用方式 GB/T 7714	Nawaz A.,Hafeez G.,Khan I.,et al. An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid[J],2020,8.
APA	Nawaz A..,Hafeez G..,Khan I..,Jan K.U..,Li H..,...&Wadud Z..(2020).An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid.IEEE Access,8.
MLA	Nawaz A.,et al."An Intelligent Integrated Approach for Efficient Demand Side Management with Forecaster and Advanced Metering Infrastructure Frameworks in Smart Grid".IEEE Access 8(2020).