A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Khalid, Zubair; Abbas, Ghulam; Awais, Muhammad; Alquthami, Thamer; Rasheed, Muhammad Babar

doi:10.3390/en13051062

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…An Artificial Neural Network (ANN) [22, 23] is a mathematical model that mimics how neurons operate in organisms. It is a very powerful tool for establishing nonlinear models.…”

Section: Artificial Neural Network: a Backgroundmentioning

confidence: 99%

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

Huang

Chen

et al. 2020

Preprint

100

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COVID-19 is spreading all across the globe. Up until March 23, 2020, the confirmed cases in 173 12 countries and regions of the globe had surpassed 346,000, and more than 14,700 deaths had resulted. The 13 confirmed cases outside of China had also reached over 81,000, with over 3,200 deaths. In this study, a 14 Convolutional Neural Network (CNN) was proposed to analyze and predict the number of confirmed cases. 15 Several cities with the most confirmed cases in China were the focus of this study, and a COVID-19 16 forecasting model, based on the CNN deep neural network method, was proposed. To compare the overall 17 efficacies of different algorithms, the indicators of mean absolute error and root mean square error were 18 applied in the experiment of this study. The experiment results indicated that compared with other deep 19 learning methods, the CNN model proposed in this study has the greatest prediction efficacy. The feasibility 20 and practicality of the model in predicting the cumulative number of COVID-19 confirmed cases were also 21 verified in this study.22

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“…An Artificial Neural Network (ANN) [22, 23] is a mathematical model that mimics how neurons operate in organisms. It is a very powerful tool for establishing nonlinear models.…”

Section: Artificial Neural Network: a Backgroundmentioning

confidence: 99%

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

Huang

Chen

et al. 2020

Preprint

100

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“…Compared with the ST method, under BS and OP, there were fewer tasks processed during DR moments, including 6 : 00-7:00 and 17 : 00-20 : 00. is is because the datacenter actively reacted to the DR signals in the first stage and conducted task scheduling. Although the BS strategy can (3) while i ≤ I do (4) for j � 1 to size(P i ) do (5) evaluate f(ind j ) for P i (6) end for (7) ind_best←the best individual (8) while size(newPop) < M do (9) if random(0, 1) < p c do (10) choose individual par1, par2 from P i (11) determine the crossover point randomly (12) crossover par1 and par2 (13) generate two new individuals x1, x2 (14) end if (15) if random(0, 1) < p m do (16) choose individual par3 from P i (17) mutate par3 at a random point (18) generate a new individual x3 (19) end if (20) newPop← [newPop,x1,x2,x3] 8 Mathematical Problems in Engineering reduce the execution of tasks at the DR time, the datacenter may be punished by reducing the execution of some tasks. is is because the BS strategy does not consider whether the task can be postponed.…”

Section: Forecasting Electricity Pricesmentioning

confidence: 99%

“…erefore, the power grid should conduct more plans to attract more users to participate in demand response projects. Demand response conducts economic incentives aimed at balancing energy demand during critical demand periods [11]. As a commodity, electricity has its own particularity.…”

Section: Introductionmentioning

confidence: 99%

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

Wang

Luo

2020

Mathematical Problems in Engineering

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Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

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“…In this paper, the objective function is solved using a non-dominated sorting genetic algorithm to minimize power losses and voltage deviation. In [20] regardless of RESs and the potential role of EVs, a dynamic energy management model is proposed to optimally schedule the network load demand and energy storage systems using machine learning and optimization algorithms. Results validate the effectiveness of this model in minimizing the network operating costs and modifying the network demand curve.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

et al. 2020

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The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.Energies 2020, 13, 1706 2 of 25 microgrids is evolving quickly [1]. The advanced structure of the microgrids allows the distribution network operators (DSOs) to consider the EV battery, either as a load or distributed generator [2]. However, deployment of RESs and EVs creates some challenges, e.g., poor reliability and power quality problems. One of the most effective ways to mitigate these challenges is the deployment of energy storage systems (ESSs) in distribution grids. ESSs can alleviate the negative effects of uncertainties in RESs production [3]. On the other hand, despite the remarkable advantages of these units, there are some challenges in terms of adequate DERs capacity requirement, system configuration, and energy management and control. One of the most challenging issues is the optimal coordination of both supply and demand sides in microgrids with the main grid, while satisfying system constraints.Currently, extensive studies have been done on the impacts of the RESs, EVs, and ESSs on the network operation . In [4], a smart home energy management strategy is proposed to realize cost-effective energy systems for customers and provide reactive power compensation for home appliances using EV and ESS. In this paper, network-level studies are limited to investigating the power factor indicator, while the potential role of EVs and ESSs in distribution networks has not been considered. Researchers in [5] presented the centralized control strategy to manag...

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A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid

Cited by 15 publications

References 38 publications

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

Multiple-Input Deep Convolutional Neural Network Model for COVID-19 Forecasting in China

Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

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