Reinforcement Learning (RL) has emerged as a promising solution for defining the optimal dispatch of Energy Storage Systems (ESS) in distributed energy systems. However, a notable gap exists in the literature: a lack of comprehensive and fair comparisons between different RL algorithms, particularly between linear and nonlinear approaches. This study critically evaluates the trade-offs between computational efficiency and operational accuracy among various Linear RL (LRL) strategies and compares them against the nonlinear Deep-Q-Network (DQN) algorithm. Through a comprehensive analysis, this study benchmarks the model-based Mixed-Integer Linear Programming (MILP) results to assess and compare these algorithms’ convergence, training efficiency, and optimization accuracy. Results indicate that while LRL approaches the operational cost accuracy of DQN, it faces significant trade-offs in computational efficiency and struggles with generalization across larger and varied datasets. The results illuminate critical areas for further development in LRL methodologies, particularly in enhancing their adaptability and generalization capabilities.
Tidal range energy is a renewable energy source that harvests the gravitational potential energy of seawater derived from tide level variation. Tidal energy generation is highly predictable and can be used as a reliable energy source. The generation capability of a tidal range scheme could be increased by utilising reversible turbines with pumping functions that increase the water level difference. Furthermore, adopting an adjustable number of active turbines during generating and pumping can help the tidal range scheme better regulating its electricity generation. Therefore, this paper proposed an optimal operation model of a tidal range scheme with reversible turbines, in which the tidal range scheme could work with a flexible generation plan to maximise its total energy generation or daily revenue. A case study is conducted to demonstrate how the operation profiles, generation and revenue vary when the tidal range scheme incorporates the pumping turbines and adopts a flexible operation plan.
The rising need for energy flexibility has paved the way to explore different solutions in various sectors. The residential sector represents one group of consumers who have very low individual flexibility, but due to their large numbers when coupled together, can help the energy system during times of need. This study investigates residential consumers’ preferences for Demand Response (DR) programs, aiming to debunk prevalent myths and provide insights for stakeholders. Analyzing data across demographic divisions like gender, age, education level, household composition, and type, our findings challenge the existing literature, highlighting the need for tailored strategies to effectively promote residential DR adoption. This study not only offers valuable insights into residential DR motivators but also underscores the importance of adapting strategies to reflect the consumer background and meet evolving consumer needs.
Wireless long-term evolution (LTE) networks are considered as a viable option for deployment in smart distribution systems due to their reliability, scalability, and cost-effectiveness. Yet, co-simulation platforms with the ability to analyze the performance of wireless LTE networks are lacking in the existing literature. This paper, for the first time, presents a real-time co-simulation platform based on Typhoon HIL and OMNeT++ for wireless LTE networks in smart distribution systems. The performance of wireless LTE networks in smart distribution systems is investigated using the proposed co-simulation platform. A case study related to distribution automation is used to demonstrate the usefulness of the proposed co-simulation platform for examining the performance of wireless LTE networks.
This work primarily focuses on investigating how the frequency response of a power system may change due to the addition or failure of tie-lines. The proposed method enables the analysis of two specific scenarios: firstly, assessing the potential enhancement in frequency response by adding an extra tie-line between two areas, and secondly, evaluating the deterioration in frequency response when one tie-line is disconnected. One notable advantage of the proposed method is its reliance solely on actual frequency response measurements from individual areas, thereby eliminating the need for extensive knowledge of system parameters. The effectiveness of this method is evaluated by comparing the analytically derived frequency responses with those obtained through simulation.
