What does this research mean for the field?

Deep reinforcement learning outperforms other optimization algorithms, such as genetic algorithms and artificial neural networks, in achieving optimal techno-economic and environmental configurations for standalone hybrid renewable energy microgrids. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To determine the optimal configuration and energy storage management strategy for standalone hybrid renewable microgrids using artificial intelligence to minimize net present cost, cost of energy, and carbon emissions.

August 29, 2026Energy Storage

Energy Storage Management and Techno‐Economic Analysis of Standalone Hybrid Renewable Energy Systems Using Deep Reinforcement Learning

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Authors

SRSantosh S. Raghuwanshi KAKhaliq Ahmed SHShrunkhala Shyamkant Halve

Discussion

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Overview

Simulation study demonstrates that deep reinforcement learning optimizes cost and emissions in standalone hybrid microgrids, suggesting superior control for off-grid renewable energy systems.

Key Points

To determine the optimal configuration and energy storage management strategy for standalone hybrid renewable microgrids using artificial intelligence to minimize net present cost, cost of energy, and carbon emissions.
Simulated seven standalone microgrid configurations integrating photovoltaic panels, wind turbines, biomass generators, diesel generators, battery banks, and electric vehicles across variable weather and load conditions.
Benchmarked deep reinforcement learning (DRL) against spoonbill swarm optimization algorithm (SSOA), genetic algorithm (GA), and artificial neural networks (ANN) for techno-economic and environmental performance.
Identified the top-performing system consisting of 210 kW photovoltaic capacity, 91 kW wind turbines, 25 kW biomass generation, 265 kWh battery storage, 22 kW electric vehicle integration, and a 28 kW diesel generator.
Achieved an optimal net present cost of ₹32,345,782, a levelized cost of energy of ₹9.23/kWh, and annual carbon emissions of 408,348 kg/year.
Demonstrated that the deep reinforcement learning strategy achieved superior techno-economic, environmental, and energy storage utilization outcomes compared to alternative heuristic and neural network algorithms across all operational scenarios.

Cite This Study

Raghuwanshi et al. (2026) studied this question.

synapsesocial.com/papers/6a9299338e5d7d1fc0c10fd8 https://doi.org/10.1002/est2.70505

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August 29, 2026Energy Storage

Energy Storage Management and Techno‐Economic Analysis of Standalone Hybrid Renewable Energy Systems Using Deep Reinforcement Learning

View Full Paper

Ask AI

Bookmark

Authors

SRSantosh S. Raghuwanshi KAKhaliq Ahmed SHShrunkhala Shyamkant Halve

Discussion

Member takes

Overview

Simulation study demonstrates that deep reinforcement learning optimizes cost and emissions in standalone hybrid microgrids, suggesting superior control for off-grid renewable energy systems.

Key Points

To determine the optimal configuration and energy storage management strategy for standalone hybrid renewable microgrids using artificial intelligence to minimize net present cost, cost of energy, and carbon emissions.
Simulated seven standalone microgrid configurations integrating photovoltaic panels, wind turbines, biomass generators, diesel generators, battery banks, and electric vehicles across variable weather and load conditions.
Benchmarked deep reinforcement learning (DRL) against spoonbill swarm optimization algorithm (SSOA), genetic algorithm (GA), and artificial neural networks (ANN) for techno-economic and environmental performance.
Identified the top-performing system consisting of 210 kW photovoltaic capacity, 91 kW wind turbines, 25 kW biomass generation, 265 kWh battery storage, 22 kW electric vehicle integration, and a 28 kW diesel generator.
Achieved an optimal net present cost of ₹32,345,782, a levelized cost of energy of ₹9.23/kWh, and annual carbon emissions of 408,348 kg/year.
Demonstrated that the deep reinforcement learning strategy achieved superior techno-economic, environmental, and energy storage utilization outcomes compared to alternative heuristic and neural network algorithms across all operational scenarios.

Cite This Study

Raghuwanshi et al. (2026) studied this question.

synapsesocial.com/papers/6a9299338e5d7d1fc0c10fd8 https://doi.org/10.1002/est2.70505

View Full Paper

Ask AI

Bookmark