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Intelligent Operation and Control of Microgrids Using Multiple Reinforcement
Learning Agents
Raja Suryadevara
Penn State Harrisburg
Master of Science in Electrical Engineering
Advisor: Peter B. Idowu
Funding Source: Department of Defense, Office of Naval Research (DoD-ONR)
Research Purpose
• Distributed Control
• Solve problems with unpredictability using AI
• Reduce Costs and Pollution
• Maximize Grid Efficiency
• Maintain Power Quality
Centralized vs Distributed Generation
Central Power
Plant
Commercial Consumers
Industrial Consumers
Residential Consumers
Wind Farm
Fuel Cell
Industrial
Consumers
Residential
Consumers
Commercial
Consumers
Commercial
Consumers
Solar
Power
Commercial
Consumers
Residential
Consumers
Solar
Power
Solar
Power
Central
Power Plant
Figure 2: Centralized Generation [1] Figure 3: Distributed Generation [1]
Communications Network
Protocols:
1) TCP/IP
2) MODBUS
3) OPC UA
4) IEC 61850
Figure 5: Communications Network
Methodology
• Deep Reinforcement Learning
Formulate
Problem
Create
EnvironmentDefine
Reward
Create
Agent
Train
Agent
Validate
Agent
Deploy
Policy
Check
Results
Figure 8: Training Workflow
Figure 7: Single RL Agent Figure 9: Multi Agent Network
Conclusion
• Reliable Communications Infrastructure
• Satisfactory results with frequency control
• Motivation towards multi-agent network
• Long training time
References
[1] P. Idowu and R. Suryadevara “Hardware-based microgrid testbed to facilitate development of Distributed Energy Resource (DER) systems for sustainable growth”, ICMA-SURE 2020.
[2] “PPL Electric Utilities Power Lab”, Penn State Harrisburg, Department of Electrical Engineering. [Online]. Available: https://sites.psu.edu/microgridtestbedpsh/.
[3] U.S. Department of Energy, Office of Electricity Delivery and Energy Reliability, “The Smart Grid: An Introduction,” 2008.
[4] Sutton, R.S., and A.G. Barto. Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning series. MIT Press, 2018. Available: https://books.google.de/books?id=6DKPtQEACAAJ.
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