This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications.

This article presents a fault-tolerant control method combined with a novel power balancing approach based on model predictive control for a three-phase cascaded H-bridge inverter. The fault-tolerant control uses redundant voltage vectors and combination voltages to redistribute the output voltage under switch open-fault and short-fault conditions. To overcome the output power imbalance problem of the conventional fault-tolerant control strategy, inter- and inner-phase power balancing approaches are proposed based on novel reward functions. The effects of H-bridge cell faults on the inverter are analyzed in detail, followed by the operational principles of the proposed power balancing and fault-tolerant control methods. Hardware-based experimental results validate the effectiveness of the proposed strategy.

This article presents a path planning strategy with ant colony algorithm for series connected batteries. The motive of this paper is the increasing need for efficient and fast equalization for Lithium-ion batteries. There are many great papers on the design of the equalization circuits. However, they lack the part of path planning strategy for the balancing circuits. To solve this issue, we adopt the graph model to represent the balancing paths among different battery cells and then construct two optimal models based on the best efficiency and speed, respectively. Finally, ant colony algorithm is used to solve these two models. This makes it possible to achieve different goals according to the practical operating conditions. We validate the function of the proposed path planning strategy through an example of 13 series connected battery balancing system.

CLLC converters are widely used in bidirectional DC applications, particularly in aviation and vehicle power supplies, requiring high dynamic commutation performance. This paper presents a commutation control method for CLLC converters based on a state trajectory model. The forward and reverse state trajectory models are developed, and the optimal commutation trajectory is derived. The gate drive signal's pulse width is calculated based on the post-commutation gain. Simulations validate the proposed model's accuracy and the control method's dynamic performance, showing significant improvement over traditional linear control methods.