A Nano-Biased Energy Management Using Reinforced Learning Multi-Agent on Layered Coalition Model: Consumer Sovereignty
Trends in energy management schema have advanced into legislating consumer-centered solutions due to inclination interests for personal owned distributed energy resources at the low-voltage level. Thence, this paper proposes a tailorable energy manager tool that empowers Prosumer(s) in a nanostructured distribution network to take sole precedence when prosuming optimal services to the energy system. It too acts as an aggregator that attests cooperative energy management processes amongst Prosumers to enhance demand-side responses and economics. The suggested nano-biased energy manager engages multi-agent network as the basis coordinator for peer-to-peer advocacy in a decentralized environment. The agents were then programmed with reinforcement and extreme learning machine intelligence on a layered coalition model to compute joint decision-making processes with constraint relaxation relaxed decision constraints and policies. The problem formulations assure engagement of energy management in the liberalized market is sustainable, reliable, and non-discriminated. Computational validations were analyzed using MATLAB and Java agent development framework on four aggregated Nanogrids representing the residential, commercial, and industrial building. Results have shown positive eco-strategic managerial avenues where cooperative assets scheduling and bidding-abled decorum were autonomously acquired. Reduced operating costs were gained from energy trading profit margin due to strategic use/sell of electricity based on real-time tariff and conferred incentive packages but constrained within the mandatory obligation to demand-side management. The subsidiary, the inauguration of meshed communication infrastructure has shown adequate monitoring and commanding resolutions for decentralized Agent(s) to function collaboratively.
Open Access Status
Saifuddin, M. R.; Logenthiran, T.; Naayagi, R. T.; and Woo, W. L., "A Nano-Biased Energy Management Using Reinforced Learning Multi-Agent on Layered Coalition Model: Consumer Sovereignty" (2019). School of Engineering and Technology Publications. 353.