An effective algorithm for demand side management in smart grid for residential load

Hossain, Mir Muntasir; Zafreen, Kazi Rehnuma; Rahman, Abidur; Zamee, Muhammad Ahsan; Aziz, Tareq

doi:10.1109/icaee.2017.8255377

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…Expert-based and automatically generated rules are used to generate usage plans. An efficient algorithm is proposed in [10]. The main goal of the algorithm is to reduce the peak-to-average ratio by operating selective devices when the grid is lightly loaded.…”

Section: Related Workmentioning

confidence: 99%

“…In [4][5][6], we can find a model user-friendly and control the process accordingly, reducing the A2P ratio as a by-product. In [7][8][9][10], the authors collect consumer preferences to reduce energy load, consumer input, comfort modeling and preference collection are more suitable for smart home and automation. If we examine these two modeling approaches in more detail, we see that comfort modeling is often limited to space heating and cooling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Preferences-Based Utility Control for Smart Homes

Naseer¹,

Saleem²,

Ghafoor³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio critical, albeit difficult. Various Demand Response (DR) and Demand Side Management (DSM) systems have been proposed to reduce this ratio to 1. The main ways to achieve this are economic incentives, user comfort modeling and control, or preference-based. In this study, we propose a unique opportunistic social time approach called the Time Utility Based Control Feature (TUBCF), which uses the concept of a utility function from economics to model and control consumer devices. We propose a DR model for residential customers to reduce Peak-to-Average Ratio (PAR) and improve customer satisfaction by eliminating Appliance Wait Time (WTA) during peak periods. For PAR reduction and WTA, we propose a system architecture and mathematical formulation. Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics. Simulation results show that using this strategy can reduce A2P by 80% and improve user comfort during peak hours.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Preferences-Based Utility Control for Smart Homes

Naseer¹,

Saleem²,

Ghafoor³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…It involves the commercial and residential users participating in demand response (DR) and demand-side management (DSM) activities. The main goal of DSM is to motivate the users to shift the time of energy consumption to off-peak hours or use minimum energy during peak hours [4]. It is completely daring to ask the users to schedule their use of appliances by limiting their comfort level.…”

Section: Introductionmentioning

confidence: 99%

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Imran¹,

Hafeez²,

Khan³

et al. 2020

IEEE Access

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An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

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“…It improves residential demand-side management using an algorithm. The writers likely propose and evaluate a smart grid home load control algorithm, demonstrating their expertise [6]. It optimises energy utilisation, decreases peak demands, and increases grid reliability.…”

mentioning

confidence: 99%

Advanced Smart Grid Architecture for Sustainable Energy Integration in Modern Urban Environments

2024

IRJMETS

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A smart city coordinates resource allocation to provide a safe, efficient, and good living environment. Smooth integration of advanced technologies optimises resource consumption, notably power control. Optimising power control includes strategically placing connected devices to optimise electricity utilisation. Intelligent urban environments need recognising this issue and solving it. Multiple solutions are needed for smart city energy optimisation. However, on-going scientific debate attempts to construct a ground-breaking intelligent grid design that can gather electricity from PV, hydro, and thermal sources. A delay-aware delivery system handles the challenging challenge of real-time energy optimisation (ECRT). Optimising energy expenditure in real time matches demand and supply. This project intends to build a smart grid that regulates electrical operations and uses sustainable energy sources. The paper focuses modelling renewable energy and improving energy distribution. We want to boost smart city energy efficiency. The hybrid smart grid proposes an effective energy resource management system that blends numerous energy production sources and real-time energy expenditure optimisation. The harmonious integration of sustainable energy sources and novel control systems improves resource allocation while being sustainable. This insightful study discusses smart energy system concepts and solutions in a technologically sophisticated city. Real-time energy optimisation and sustainable energy sources show an on-going commitment to increasing efficiency, resource utilisation, and sustainable design in intelligent urban environments.

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An effective algorithm for demand side management in smart grid for residential load

Cited by 16 publications

References 15 publications

Temporal Preferences-Based Utility Control for Smart Homes

Temporal Preferences-Based Utility Control for Smart Homes

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Advanced Smart Grid Architecture for Sustainable Energy Integration in Modern Urban Environments

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