Optimally Coordinated Energy Management Framework for Profit Maximization Considering Dispatchable and Non-Dispatchable Energy Resources
Abstract
Contemporary distribution network can be seen with diverse dispatchable and non-dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non-dispatchable resources can provide several techno-economic and social benefits. Since, battery energy storage systems (BESSs) and microturbine (MT) units are capital intensive, a thorough investigation of their coordinated scheduling on pure economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty handling of non-dispatchable resources and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and MT units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility by employing a recently explored modified African buffalo optimization (MABO) algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps a track of system states as a-priori thus guides the artificial intelligence based solution technique for sequential optimization. This may also reduce the computational burden of complex real-life engineering optimization problems. Further, a novel concept of fictitious charges is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33-bus test distribution system highlights the importance of the proposed methodology.
Cite
@article{arxiv.2307.00277,
title = {Optimally Coordinated Energy Management Framework for Profit Maximization Considering Dispatchable and Non-Dispatchable Energy Resources},
author = {Rayees Ahmad Thokar and Nikhil Gupta and K. R. Niazi and Anil Swarnkar and Nand K. Meena and Jin Yang},
journal= {arXiv preprint arXiv:2307.00277},
year = {2023}
}