VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Goh, Hui Hwang"

Now showing 1 - 2 of 2
  • An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy Management
    Goh, Hui Hwang; Huang, Yifeng; Lim, Chee Shen; Zhang, Dongdong; Liu, Hui; Dai, Wei; Kurniawan, Tonni Agustiono; Rahman, Saifur (IEEE, 2022-06-01)
    Reinforcement learning based energy management strategy has been an active research subject in the past few years. Different from the baseline reward function (BRF), the work proposes and investigates a multi-stage reward mechanism (MSRM) that scores the agent's step and final performance during training and returns it to the agent in real time as a reward. MSRM will also improve the agent's training through expert intervention which aims to prevent the agent from being trapped in sub-optimal strategies. The energy management performance considered by MSRM-based algorithm includes the energy balance, economic cost, and reliability. The reward function is assessed in conjunction with two deep reinforcement learning algorithms: double deep Q-learning network (DDQN) and policy gradient (PG). Upon benchmarking with BRF, the numerical simulation shows that MSRM tends to improve the convergence characteristic, reduce the explained variance, and reduce the tendency of the agent being trapped in suboptimal strategies. In addition, the methods have been assessed with MPC-based energy management strategies in terms of relative cost, self-balancing rate, and computational time. The assessment concludes that, in the given context, PG-MSRM has the best overall performance.
  • Thumbnail Image
    Optimal Scheduling of Integrated Energy Systems With Multiple CCHPs for High Efficiency and Low Emissions
    Xie, Haimin; Liu, Hui; Wan, Can; Goh, Hui Hwang; Rahman, Saifur (IEEE, 2023-08-14)
    In order to reach carbon neutrality, there is growing interest in reducing greenhouse gas (GHG) and improving energy efficiency. One way to address this issue is the optimal scheduling of the integrated energy system (IES) with multiple combined cooling heating and power (CCHP) systems as proposed in this article. We model IES as a device with multiple input/output ports by the energy hub (EH) framework and propose a multiobjective optimal model to improve energy efficiency and reduce GHG emissions. The proposed model is constructed as a mixed-integer nonlinear programming (MINLP) due to considering nonlinear couplings of multiple energy flows and the unit commitment of multiple CCHP systems. To improve the computational efficiency, the proposed MINLP model is transformed into a nonlinear programming (NLP) model by a fast unit commitment technique based on the approximation of the aggregated online capacity. Finally, simulation results show the effectiveness of the proposed approach in reducing GHG emissions and improving energy efficiency as well as computational efficiency.