Optimum Design of Battery-Assisted Photo-Voltaic Energy System for a Commercial Application

A new online scheduling algorithm is proposed for photovoltaic (PV) systems with battery-assisted energy storage systems (BESS). The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions. The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities. The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data, and the results indicate that it outperforms existing algorithms such as Deep Q-learning. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Optimum Design of Battery-Assisted Photo-Voltaic Energy System for a Commercial Application

Cited by 2 publications

References 10 publications

Optimum Integration of Solar Energy With Battery Energy Storage Systems

Optimum Integration of Solar Energy With Battery Energy Storage Systems

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems

Contact Info

Product

Resources

About