Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy

Authors

  • Chunhua Deming Research Fellow, NUS Graduate School (NUSGS), National University of Singapore, 119077, Singapore
  • Prasanna Pasam Developer IV Specialized, Supreme Tech Solutions, Vienna, Virginia, USA
  • Abhishekar Reddy Allam Software Developer, Compunnel Software Group Inc., 103 Morgan Lane STE 102, Plainsboro, NJ, USA
  • Rahimoddin Mohammed Software Engineer, Credit Risk, UBS, 1000 Harbor Blvd, Weehawken, NJ 07086, USA
  • SSMLG Gudimetla Naga Venkata IAM Engineer, HCL Global Systems Inc., Farmington Hills, Michigan- 48335, USA
  • Kanaka Rakesh Varma Kothapalli Consultant, Yotta Systems Inc., Morristown, New Jersey, 07960, USA

DOI:

https://doi.org/10.18034/apjee.v8i2.762

Keywords:

Real-time Scheduling, Energy Optimization, Smart Grid, Renewable Energy, Grid Integration, Scheduling Algorithms, Distributed Energy Resources, Power System Optimization

Abstract

This research investigates the scheduling of tasks in real-time to optimize energy use in the context of integrating renewable energy sources into the smart grid. The primary goals are to analyze the influence of fluctuations in renewable energy on grid synchronization, evaluate the efficiency of different optimization methods, and identify significant obstacles and corresponding remedies. Secondary data studies advanced forecasting methods, energy storage systems, and optimization techniques, including Linear Programming (LP), Dynamic Programming (DP), and metaheuristics. The significant findings show that renewable energy fluctuations affect power system stability. Advanced prediction methods and energy storage are essential in reducing these impacts. Optimization approaches enhance the scheduling efficiency, but the computational complexity and practical application constraints limit their effectiveness. Challenges such as frequency regulation, voltage management, and integrating Distributed Energy Resources (DERs) need specific solutions such as dynamic voltage support and grid modernization. The policy implications include supporting advanced technologies, encouraging real-time scheduling system research, and enhancing grid infrastructure to increase resilience. These measures are essential for integrating renewable energy, ensuring a reliable smart grid, and achieving a sustainable future.

Downloads

Download data is not yet available.

References

Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687

Ahmad, A., Khan, A., Javaid, N., Hussain, H. M., Abdul, W. (2017). An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. Energies, 10(4), 549. https://doi.org/10.3390/en10040549 DOI: https://doi.org/10.3390/en10040549

Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145

Aslam, S., Iqbal, Z., Javaid, N., Khan, Z. A., Aurangzeb, K. (2017). Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes. Energies, 10(12), 2065. https://doi.org/10.3390/en10122065 DOI: https://doi.org/10.3390/en10122065

Boaro, M., Fuselli, D., Angelis, F. D., Liu, D., Wei, Q. (2013). Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management. Cognitive Computation, 5(2), 264-277. https://doi.org/10.1007/s12559-012-9191-y DOI: https://doi.org/10.1007/s12559-012-9191-y

Di Fazio, A. R., Erseghe, T., Ghiani, E., Murroni, M., Siano, P. (2013). Integration of Renewable Energy Sources, Energy Storage Systems, and Electrical Vehicles with Smart Power Distribution Networks. Journal of Ambient Intelligence and Humanized Computing, 4(6), 663-671. https://doi.org/10.1007/s12652-013-0182-y DOI: https://doi.org/10.1007/s12652-013-0182-y

Divshali, P. H., Choi, B. J., Liang, H., Söder, L. (2017). Transactive Demand Side Management Programs in Smart Grids with High Penetration of Evs. Energies, 10(10). https://doi.org/10.3390/en10101640 DOI: https://doi.org/10.3390/en10101640

Javaid, N., Fahim, A., Ullah, I., Abid, S., Abdul, W. (2017). Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid. Energies, 10(10). https://doi.org/10.3390/en10101546 DOI: https://doi.org/10.3390/en10101546

Javaid, N., Hussain, S. M., Ullah, I., Noor, M. A., Abdul, W. (2017). Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations. Energies, 10(8), 1131. https://doi.org/10.3390/en10081131 DOI: https://doi.org/10.3390/en10081131

Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95

Kothapalli, K. R. V. (2019). Enhancing DevOps with Azure Cloud Continuous Integration and Deployment Solutions. Engineering International, 7(2), 179-192. DOI: https://doi.org/10.18034/ei.v7i2.721

Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017a). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93

Mohammed, M. A., Mohammed, R., Pasam, P., & Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162. https://doi.org/10.18034/abcjar.v7i2.755 DOI: https://doi.org/10.18034/abcjar.v7i2.755

Mohammed, R. & Pasam, P. (2020). Autonomous Drones for Advanced Surveillance and Security Applications in the USA. NEXG AI Review of America, 1(1), 32-53.

Mohammed, R. (2021). Code Refactoring Strategies for Enhancing Robotics Software Maintenance. International Journal of Reciprocal Symmetry and Theoretical Physics, 8, 41-50. https://upright.pub/index.php/ijrstp/article/view/152

Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142

Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751 DOI: https://doi.org/10.18034/ajhal.v6i2.751

Park, L., Jang, Y., Bae, H., Lee, J., Park, C. Y. (2017). Automated Energy Scheduling Algorithms for Residential Demand Response Systems. Energies, 10(9), 1326. https://doi.org/10.3390/en10091326 DOI: https://doi.org/10.3390/en10091326

Pilz, M., Al-Fagih, L., Pfluegel, E. (2017). Energy Storage Scheduling with an Advanced Battery Model: A Game–Theoretic Approach. Inventions, 2(4). https://doi.org/10.3390/inventions2040030 DOI: https://doi.org/10.3390/inventions2040030

Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151

Soares, T., Silva, M., Sousa, T., Morais, H., Vale, Z. (2017). Energy and Reserve under Distributed Energy Resources Management-Day-Ahead, Hour-Ahead and Real-Time. Energies, 10(11), 1778. https://doi.org/10.3390/en10111778 DOI: https://doi.org/10.3390/en10111778

Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149

Downloads

Published

2021-11-05

How to Cite

Deming, C., Pasam, P., Allam, A. R., Mohammed, R., Venkata, S. G. N., & Kothapalli, K. R. V. (2021). Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy. Asia Pacific Journal of Energy and Environment, 8(2), 77-88. https://doi.org/10.18034/apjee.v8i2.762

Similar Articles

61-69 of 69

You may also start an advanced similarity search for this article.