A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

Hafeez, Ghulam; Islam, Noor; Ali, Ammar; Ahmad, Salman; Alimgeer, Muhammad Usman and Khurram Saleem

doi:10.3390/pr7080499

Cited by 44 publications

(16 citation statements)

References 39 publications

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“…They have achieved 59.06% and 17.40% reduction in bill cost and PAR respectively. In [35], an optimal load scheduling framework based on hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm has been suggested. They scheduled the household load using the output of the forecaster module.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

et al. 2021

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Existing power grids (PGs) and in-home energy management controllers do not offer its users the choice to maintain comfort and provide a bearable solution in terms of low cost and reduced carbon emission. This work is based on energy usage scheduling and management under electric utility and renewable energy sources i.e., solar energy (SE), controllable heat and power (CHP) and wind energy (WE) together. Efficient integration of renewable energy sources (RES) and battery storage system (BSS) have been suggested to solve the energy management problem, reduce the bill cost, peak-to-average ratio (PAR) and carbon emission. User's electricity bill reduction have been achieved by proposed power usage scheduling method and integrating low cost RESs. PAR minimization have been achieved through shifting the demand in response to real time price from high-peak hours to low-peak hours. In this context, load scheduling and energy storage system management controller (LSEMC) is proposed which is based on heuristic algorithms i.e., genetic algorithm (GA), wind driven optimization (WDO), binary particle swarm optimization (BPSO), bacterial foraging optimization (BFO) and our suggested hybrid of GA, WDO and PSO (HGPDO) algorithm. The performance of the heuristic algorithms and proposed scheme is evaluated numerically. Results demonstrate that our proposed algorithm and the LSEMC reduces the electricity bill, PAR and CO 2 in Case 1, by 58.69%, 52.78% and 72.40%, in Case 2, by 47.55%, 45.02% and 92.90% and in Case 3, by 33.6%, 54.35% and 91.64%, respectively as compared with unscheduled. Moreover, the user comfort by our proposed HGPDO algorithm in terms of delay, thermal, air quality and visual improves by 35.55%, 16.66%, 91.64% and 45%, respectively. INDEX TERMS Energy management; battery energy storage systems; renewable; hybrid heuristic algorithms; power usage scheduling; smart grid.

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Section: Related Workmentioning

confidence: 99%

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

et al. 2021

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“…In the input-output efficiency analysis of vegetable industry, according to the characteristics of vegetable industry production, land (S), capital (K), and labor (L) are three main factors of the production. So the production function of the vegetable industry can be written as Equation (2). The characteristics of the production function are conciseness, operability, and wide application.…”

Section: Production Function Of Vegetable Industrymentioning

confidence: 99%

“…The main potential benefits of green technologies are that they can significantly reduce the cost of carbon dioxide emissions, reduce energy waste, and improve environmental performance. Some researchers have paid attention to the energy saving and waste reduction in production processes based on green technology [1,2]. However, few researchers consider that adjusting input factors can save energy and reduce waste from the perspective of green sustainability in vegetable production.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Production Process Efficiency of Provincial Greenhouse Vegetables in China Using Data Envelopment Analysis: A Green and Sustainable Perspective

et al. 2019

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The evaluation of vegetable production process efficiency is of great significance for energy saving and waste reduction in production processes. However, few studies have considered the effect of greenhouse vegetable production process efficiency on energy saving and waste reduction. In this paper, data envelopment analysis (DEA) is used to analyze the production process efficiency and the effective use of input elements of greenhouse vegetables at the provincial level in China. The results reveal that many chemical fertilizers, farmyard manure, and pesticides in China are inefficient. On the other hand, the pure technical efficiency of greenhouse tomatoes and cucumbers is low in most areas of China. Meanwhile, the scale efficiency of greenhouse eggplants and greenhouse peppers is low in most areas of China. In order to save energy and develop green sustainable agriculture, we put forward some suggestions to improve the production efficiency of greenhouse vegetables in different provinces.

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“…(SGs) [1]- [3]. This renovation has created a platform for both agents to forecast electrical energy consumption for decision making in the SG.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

et al. 2020

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Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in the smart grid (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed. INDEX TERMS Electrical energy consumption forecasting, energy management, smart grid, grey correlation analysis, differential evolution, radial basis kernel-based principal component analysis, support vector machine.

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A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

Cited by 44 publications

References 39 publications

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

An Optimal Power Usage Scheduling in Smart Grid Integrated With Renewable Energy Sources for Energy Management

Evaluating Production Process Efficiency of Provincial Greenhouse Vegetables in China Using Data Envelopment Analysis: A Green and Sustainable Perspective

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

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