Smart energy management for revenue optimization and grid independence in an Indian RDS

• Proposes a smart energy system for efficient load scheduling and cost reduction. • Evaluates energy use across residential, commercial, and industrial sectors. • Integrates solar, wind, EVs, and batteries to enhance grid resilience. • Employs advanced optimization for improved energy management and revenue. • Supports surplus renewable energy trade through a feed-in tariff mechanism. This study presents a novel smart energy management framework for the Indian 28-bus radial distribution system (RDS), optimizing energy consumption across residential, commercial, and industrial sectors. The framework employs the hunter-prey optimization algorithm (HPOA) to enhance appliance scheduling, renewable energy integration (PV, WT, EV, BESS), and dynamic tariff management while addressing uncertainties in electric vehicle (EV) usage and renewable distributed generation (RDG) output. By incorporating photovoltaic (PV) systems, wind turbines (WT), electric vehicles (EVs), and battery energy storage systems (BESS), the system maximizes renewable energy utilization, reducing grid dependency and improving cost-effectiveness. HPOA ensures efficient scheduling, balancing user comfort, cost savings, and revenue generation through real-time pricing (RTP) and feed-in tariffs. The system effectively manages EV and RDG uncertainties, optimizing surplus energy redirection to the grid, thereby enhancing economic viability. A comparative analysis with alternative optimization algorithms demonstrates HPOA’s superiority in convergence speed, computational efficiency, and energy cost reduction. Additionally, the study evaluates the levelized cost of energy (LCOE), confirming the economic feasibility of the proposed model. The results indicate a significant reduction in electricity costs and grid dependence, yielding a total revenue of ₹ 20,982.00—comprising ₹ 2,042.64 from residential, ₹ 4,780.98 from commercial, and ₹ 7,158.38 from industrial sectors. These findings underscore the financial and sustainability advantages of implementing smart energy management strategies in evolving energy landscapes.

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Publication Details

Journal
Energy Conversion and Management X
Published
2025-03-08
DOI
https://doi.org/10.1016/j.ecmx.2025.100955
Citations
7
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
5.11
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article

Smart energy management for revenue optimization and grid independence in an Indian RDS

Sanjeevikumar Padmanaban, T. Yuvaraj, Thanikanti Sudhakar Babu, M. Thirumalai et al.
7 citations
Energy Conversion and Management X
Smart Grid Energy Management
5.11
article

Smart energy management for revenue optimization and grid independence in an Indian RDS

Sanjeevikumar Padmanaban, T. Yuvaraj, Thanikanti Sudhakar Babu, M. Thirumalai, M. Dharmalingam
article en
7 citations

Abstract

• Proposes a smart energy system for efficient load scheduling and cost reduction. • Evaluates energy use across residential, commercial, and industrial sectors. • Integrates solar, wind, EVs, and batteries to enhance grid resilience. • Employs advanced optimization for improved energy management and revenue. • Supports surplus renewable energy trade through a feed-in tariff mechanism. This study presents a novel smart energy management framework for the Indian 28-bus radial distribution system (RDS), optimizing energy consumption across residential, commercial, and industrial sectors. The framework employs the hunter-prey optimization algorithm (HPOA) to enhance appliance scheduling, renewable energy integration (PV, WT, EV, BESS), and dynamic tariff management while addressing uncertainties in electric vehicle (EV) usage and renewable distributed generation (RDG) output. By incorporating photovoltaic (PV) systems, wind turbines (WT), electric vehicles (EVs), and battery energy storage systems (BESS), the system maximizes renewable energy utilization, reducing grid dependency and improving cost-effectiveness. HPOA ensures efficient scheduling, balancing user comfort, cost savings, and revenue generation through real-time pricing (RTP) and feed-in tariffs. The system effectively manages EV and RDG uncertainties, optimizing surplus energy redirection to the grid, thereby enhancing economic viability. A comparative analysis with alternative optimization algorithms demonstrates HPOA’s superiority in convergence speed, computational efficiency, and energy cost reduction. Additionally, the study evaluates the levelized cost of energy (LCOE), confirming the economic feasibility of the proposed model. The results indicate a significant reduction in electricity costs and grid dependence, yielding a total revenue of ₹ 20,982.00—comprising ₹ 2,042.64 from residential, ₹ 4,780.98 from commercial, and ₹ 7,158.38 from industrial sectors. These findings underscore the financial and sustainability advantages of implementing smart energy management strategies in evolving energy landscapes.

Energy Conversion and Management XVol. 26
Rajamangala University of Technology (TH), University of South-Eastern Norway (NO), Saveetha University (IN)
Affordable and clean energy
Openalex Percentile: Top 5%
Smart Grid Energy Management
5.11
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