Risk Management in Robotic Process Automation

Yendluri, Dileep Kumar; Thatikonda, Ramya; Thatikonda, Reshmi; Ponnala, Jainath; Kempanna, M.; Bhuvanesh, A.

doi:10.1109/iatmsi60426.2024.10503443

2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) 2024

DOI: 10.1109/iatmsi60426.2024.10503443

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Risk Management in Robotic Process Automation

Dileep Kumar Yendluri,

Ramya Thatikonda,

Reshmi Thatikonda

et al.

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2024

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Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

Guo,

Chen,

et al. 2024

Atmosphere

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The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals.

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Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

Guo,

Chen,

et al. 2024

Atmosphere

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Risk Management in Robotic Process Automation

Cited by 1 publication

References 6 publications

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

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