An Efficient Energy Management in Smart Grid Considering Demand Response Program and Renewable Energy Sources

Rehman, Ateeq Ur; Hafeez, Ghulam; Albogamy, Fahad R.; Wadud, Zahid; Ali, Faheem; Khan, Imran; Rukh, Gul; Khan, Sheraz

doi:10.1109/access.2021.3124557

Cited by 31 publications

(5 citation statements)

References 54 publications

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“…By incentivizing consumers to reduce their electricity usage during peak periods, demand response programs help avoid grid congestion and reduce the need for costly infrastructure upgrades. Additionally, smart grids enable utilities to implement load management strategies, such as shifting non-essential loads to off-peak times, further improving system efficiency (Du, Lu & Zhong, 2019, Rehman, et. al., 2021, Shakeri, et.…”

Section: Impact Of Smart Grids On Energy Efficiencymentioning

confidence: 99%

The Impact of Smart Grids on Energy Efficiency: A Comprehensive Review

Tosin Michael Olatunde,

Azubuike Chukwudi Okwandu,

Dorcas Oluwajuwonlo Akande

et al. 2024

Eng. sci. technol. j.

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Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector. Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

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Section: Impact Of Smart Grids On Energy Efficiencymentioning

confidence: 99%

The Impact of Smart Grids on Energy Efficiency: A Comprehensive Review

Tosin Michael Olatunde,

Azubuike Chukwudi Okwandu,

Dorcas Oluwajuwonlo Akande

et al. 2024

Eng. sci. technol. j.

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“…This is because they are not always available from individual sources. They use emission factors, which are representative values linking the quantity of the atmosphere pollutant to the type of activity (energy production) associated with their emissions [23,36,47,48,55,87,87]. Usually, these are the averages of the available data of acceptable quality.…”

Section: Rq4 What Are the Optimization Objectives Of The Cited Algori...mentioning

confidence: 99%

“…Instead of being an appliance-shifting problem, the customer schedules their appliances while respecting certain functioning time intervals. They have the possibility to use battery/renewable energy, as a wildcard, to reduce the utility energy peak [36,37,47].…”

Section: Accurate Renewable Energy and Storage Systems Integrationmentioning

confidence: 99%

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Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Mimi,

Ben Maissa,

Tamtaoui

2023

Smart Cities

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Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.

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“…They have used state-of-the-art methods for this, including enhanced differential evolution (EDE), GA, firefly optimization algorithm (FOA), rainfall optimization algorithm (RFOA), optimal stopping rule (OSR), game theory, and teacher learning-based optimization (TLBO). In [50] and [51], authors have proposed a DSM-based HEMS that adopt the hybrid of GA, PSO, and WDO to curtail PAR, energy cost, and carbon emission and achieve maximum UC. In [52] and [53] an online and real-time energy management controller is suggested for grid-connected sustainable smart home considering UC.…”

Section: Introductionmentioning

confidence: 99%

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

et al. 2022

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A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO 2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.INDEX TERMS energy management controller, user-comfort, demand shifting, load scheduling, battery storage systems, demand response, solar energy, smart grid. NOMENCLATURE

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An Efficient Energy Management in Smart Grid Considering Demand Response Program and Renewable Energy Sources

Cited by 31 publications

References 54 publications

The Impact of Smart Grids on Energy Efficiency: A Comprehensive Review

The Impact of Smart Grids on Energy Efficiency: A Comprehensive Review

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

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