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Optimized energy management for profitability

Paper Authors:

Rayees Ahmad Thokar,

Nikhil Gupta,

K. R. Niazi,

Anil Swarnkar,

Nand K. Meena,

Jin Yang


Key Details

Proposes optimizing utility profit with distributed battery and turbine assets

Uses adaptive battery scheduling based on daily average electricity price

Employs a decision system to guide optimization algorithm across system states

Achieves higher profit and coordination than a fixed battery scheduling method

Limits uneconomic battery usage through novel 'fictitious charges' concept

AI generated summary

Optimized energy management for profitability

This paper proposes a framework to optimally schedule battery storage systems and microturbines in a distribution grid alongside existing solar and wind resources. The goal is maximizing the utility's daily profit function under dynamic electricity pricing. An adaptive scheduling approach adjusts battery charge/discharge times based on average electricity price to exploit price arbitrage. Optimization uses a bio-inspired algorithm guided by a decision mechanism that processes system states. Results demonstrate enhanced profitability and coordination of distributed resources versus an existing strategy.

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