DeepAI AI Chat
Log In Sign Up

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

by   Tobi Michael Alabi, et al.

The carbon-capturing process with the aid of CO2 removal technology (CDRT) has been recognised as an alternative and a prominent approach to deep decarbonisation. However, the main hindrance is the enormous energy demand and the economic implication of CDRT if not effectively managed. Hence, a novel deep reinforcement learning agent (DRL), integrated with an automated hyperparameter selection feature, is proposed in this study for the real-time scheduling of a multi-energy system coupled with CDRT. Post-carbon capture systems (PCCS) and direct-air capture systems (DACS) are considered CDRT. Various possible configurations are evaluated using real-time multi-energy data of a district in Arizona and CDRT parameters from manufacturers' catalogues and pilot project documentation. The simulation results validate that an optimised soft-actor critic (SAC) algorithm outperformed the TD3 algorithm due to its maximum entropy feature. We then trained four (4) SAC agents, equivalent to the number of considered case studies, using optimised hyperparameter values and deployed them in real time for evaluation. The results show that the proposed DRL agent can meet the prosumers' multi-energy demand and schedule the CDRT energy demand economically without specified constraints violation. Also, the proposed DRL agent outperformed rule-based scheduling by 23.65 configuration with PCCS and solid-sorbent DACS is considered the most suitable configuration with a high CO2 captured-released ratio of 38.54, low CO2 released indicator value of 2.53, and a 36.5 waste heat utilisation and high absorption capacity of the selected sorbent. However, the adoption of CDRT is not economically viable at the current carbon price. Finally, we showed that CDRT would be attractive at a carbon price of 400-450USD/ton with the provision of tax incentives by the policymakers.


page 13

page 16

page 20

page 22

page 25

page 26

page 29

page 30


Demand-Side Scheduling Based on Deep Actor-Critic Learning for Smart Grids

We consider the problem of demand-side energy management, where each hou...

A Human Mixed Strategy Approach to Deep Reinforcement Learning

In 2015, Google's DeepMind announced an advancement in creating an auton...

Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning

In delay-sensitive industrial internet of things (IIoT) applications, th...

Deep Reinforcement Learning for Artificial Upwelling Energy Management

The potential of artificial upwelling (AU) as a means of lifting nutrien...

Consolidated Adaptive T-soft Update for Deep Reinforcement Learning

Demand for deep reinforcement learning (DRL) is gradually increased to e...